Essential-Web v1.0
Collection
10 items
•
Updated
•
8
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Could not read the parquet files: Failed reading metadata buffer (requested 71060 bytes but got 0 bytes)
Error code: FileSystemError
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id
int64 | text
string | metadata
dict | line_start_n_end_idx
dict | quality_signals
dict | eai_taxonomy
dict | pid
string |
|---|---|---|---|---|---|---|
6,041,369,874,394,701,000 |
Cant access local https site
My domain is: mctrees.net
My operating system is (include version): windows server 2016 (host server), windows 10(client), using google chrome
My web server is (include version): IIS 10.0.14393.0
I can login to a root shell on my machine (yes or no, or I don’t know): its windows
I’m using a control panel to manage my site (no, or provide the name and version of the control panel):IIS
I have a https website running in IIS with a letsencrypt certificate, it is running on my local network, with ports 80 and 443 forwarded, however I cannot access the site, but if I ask one of my friends to go to it they can access it, using https, and also when I use my phones mobile data to access the website I am connected.
om my computer it lust says connecting, and then that the connection timed out.
is there any way to maybe preinstall the certs on my pc so that i can access them, or is there some settings in iis that I should enable?
thanks
Hi @trebot97351,
Particularly if you can’t access the site on port 80 with HTTP, this is probably a firewall or router configuration issue rather than a certificate or HTTPS issue. You should look into how the ports are being forwarded and how the DNS is set up. The certificate wouldn’t be used at all when accessing the site on port 80!
I can access it on HTTP, but not on HTTPS, I could when using a self signed certificate though.
Simply put - you and everyone else can access it fine from the external network via forwarded ports, but you can't access it from your local network. If this is the case, the question is how do you access it from your LAN? By the same domain name as you would do externally or by some different name (maybe even IP)?
I use the domain name (mctrees.net), same as i use externally
Does it get resolved into an external or internal IP and is that IP correct? Try
nslookup mctrees.net
from the command line. If the IP is correct, try connecting using HTTPS to that IP address (disregard errors related to mismatched name if you manage to connect).
Does it resolve to the same IP address from the LAN as from the Internet? Is that the same IP address that it actually uses on the LAN, or does it use a private IP address internally?
In the latter case, it seems likely that the port forwarding for port 443 somehow only applies to the router’s public-facing Internet interface and not to the internal interface. (Another alternative would be to create a hosts file on your own computer giving the server’s internal IP address.)
Server: BTHomeHub.home
Address: 192.168.1.254
Non-authoritative answer:
Name: mctrees.net
Address: 81.141.34.50
thats what it returns on LAN
So, it’s probably going out through your router and then back in again. One thing to look at is whether the port forwarding for port 443 is set up in exactly the same way as the port forwarding for port 80.
and this on mobile data:
Server: UnKnown
Address: 192.168.43.1
Non-authoritative answer:
Name: mctrees.net
Address: 81.141.34.50
thats my port forwarding rules
Looks suspiciously similar to https://serverfault.com/questions/55611/loopback-to-forwarded-public-ip-address-from-local-network-hairpin-nat
True, but why should the behavior be different between one TCP port and another?
Ok, how would I set up a hairpin NAT on my pc. or would I need to set it up on my router?
Indeed, it should not be (at first it looked like client isolation feature but in that case I'd also expect 80 and 443 to behave in the same manner). Unless this is router-specific somehow.
@trebot97351, you didn't say if you were able to connect via HTTPS when using IP instead of the name - did that work?
@leader I can access it via the local IP address, but in chrome it says that it isn’t secure, probably because im using the certificate for the domain to access it via the IP
You’ll probably do well with a hosts file.
https://support.rackspace.com/how-to/modify-your-hosts-file/
You can point it at the local IP address and then your computer will use that IP address whenever it tries to access that name.
Hi @trebot97351
Lets just slow down for a minute.
A) You need to articulate your entire network setup. For example are you using an internal DNS server.
B) Below is highly unlikely on mobile data. 192.168.x.x IPs are private IP Ranges which means the DNS response is not coming from a web accessible DNS server
Server: UnKnown
Address: 192.168.43.1
Non-authoritative answer:
Name: mctrees.net
Address: 81.141.34.50
C) Depending on how you setup your IIS server you may only be able to access a site via a DNS name (not IP)
D) If you can access your website via IP the certificate will not be valid as it only has a DNS Name.
E)> I can access it on HTTP, but not on HTTPS, I could when using a self signed certificate though.
Can you paste the actual screenshot of what is in your browsers error message
There are lots of conflicting facts
Andrei
This topic was automatically closed 30 days after the last reply. New replies are no longer allowed.
|
{
"url": "https://community.letsencrypt.org/t/cant-access-local-https-site/33642",
"source_domain": "community.letsencrypt.org",
"snapshot_id": "CC-MAIN-2024-30",
"warc_metadata": {
"Content-Length": "46462",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:Q3GT52PGRZRMEGMCDKRPIJ5OD3TDBVAB",
"WARC-Concurrent-To": "<urn:uuid:099c0104-95e6-47b8-8e46-f72e29bd4653>",
"WARC-Date": "2024-07-23T12:36:07Z",
"WARC-IP-Address": "184.105.99.43",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:UEF727FB2MQSC5FMQY6B4DFGRG42GMQT",
"WARC-Record-ID": "<urn:uuid:beb33491-42ed-4b2c-bbfc-dbb224e1470b>",
"WARC-Target-URI": "https://community.letsencrypt.org/t/cant-access-local-https-site/33642",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:1c0fb0ad-2ed2-4fca-b658-7381bd3a62ee>"
},
"warc_info": "isPartOf: CC-MAIN-2024-30\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for July 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-174\r\nsoftware: Apache Nutch 1.20 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
29,
30,
56,
173,
226,
310,
417,
418,
746,
826,
964,
965,
972,
973,
990,
991,
1313,
1314,
1410,
1411,
1728,
1729,
1791,
1792,
1873,
1874,
1895,
1896,
2059,
2060,
2244,
2245,
2540,
2541,
2564,
2587,
2588,
2614,
2632,
2654,
2655,
2684,
2685,
2892,
2893,
2918,
2919,
2935,
2957,
2958,
2984,
3002,
3024,
3025,
3056,
3057,
3198,
3199,
3280,
3281,
3371,
3372,
3562,
3563,
3681,
3682,
3857,
3858,
3901,
3902,
3963,
3964,
4092,
4093,
4109,
4110,
4144,
4145,
4248,
4406,
4407,
4423,
4445,
4446,
4472,
4490,
4512,
4513,
4621,
4622,
4724,
4725,
4825,
4826,
4904,
4905,
4941,
4942,
4949,
4950
],
"line_end_idx": [
29,
30,
56,
173,
226,
310,
417,
418,
746,
826,
964,
965,
972,
973,
990,
991,
1313,
1314,
1410,
1411,
1728,
1729,
1791,
1792,
1873,
1874,
1895,
1896,
2059,
2060,
2244,
2245,
2540,
2541,
2564,
2587,
2588,
2614,
2632,
2654,
2655,
2684,
2685,
2892,
2893,
2918,
2919,
2935,
2957,
2958,
2984,
3002,
3024,
3025,
3056,
3057,
3198,
3199,
3280,
3281,
3371,
3372,
3562,
3563,
3681,
3682,
3857,
3858,
3901,
3902,
3963,
3964,
4092,
4093,
4109,
4110,
4144,
4145,
4248,
4406,
4407,
4423,
4445,
4446,
4472,
4490,
4512,
4513,
4621,
4622,
4724,
4725,
4825,
4826,
4904,
4905,
4941,
4942,
4949,
4950,
5050
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 5050,
"ccnet_original_nlines": 100,
"rps_doc_curly_bracket": 0,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.4307149052619934,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.056487198919057846,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.20035305619239807,
"rps_doc_frac_unique_words": 0.3588571548461914,
"rps_doc_mean_word_length": 4.504000186920166,
"rps_doc_num_sentences": 69,
"rps_doc_symbol_to_word_ratio": 0,
"rps_doc_unigram_entropy": 5.153709411621094,
"rps_doc_word_count": 875,
"rps_doc_frac_chars_dupe_10grams": 0.07866024225950241,
"rps_doc_frac_chars_dupe_5grams": 0.12179650366306305,
"rps_doc_frac_chars_dupe_6grams": 0.10809438675642014,
"rps_doc_frac_chars_dupe_7grams": 0.09439229220151901,
"rps_doc_frac_chars_dupe_8grams": 0.07866024225950241,
"rps_doc_frac_chars_dupe_9grams": 0.07866024225950241,
"rps_doc_frac_chars_top_2gram": 0.016239529475569725,
"rps_doc_frac_chars_top_3gram": 0.013955850154161453,
"rps_doc_frac_chars_top_4gram": 0.009134739637374878,
"rps_doc_books_importance": -434.17041015625,
"rps_doc_books_importance_length_correction": -434.17041015625,
"rps_doc_openwebtext_importance": -242.6963348388672,
"rps_doc_openwebtext_importance_length_correction": -242.6963348388672,
"rps_doc_wikipedia_importance": -249.5316925048828,
"rps_doc_wikipedia_importance_length_correction": -249.5316925048828
},
"fasttext": {
"dclm": 0.41476601362228394,
"english": 0.9320796728134155,
"fineweb_edu_approx": 1.9924973249435425,
"eai_general_math": 0.49750345945358276,
"eai_open_web_math": 0.11435127258300781,
"eai_web_code": 0.740272045135498
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "004.678",
"labels": {
"level_1": "General works, books and libraries, information sciences",
"level_2": "",
"level_3": "Computers and Computer science"
}
},
"secondary": {
"code": "005.1",
"labels": {
"level_1": "General works, books and libraries, information sciences",
"level_2": "",
"level_3": "Computer programming"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "3",
"label": "Apply"
},
"secondary": {
"code": "2",
"label": "Understand"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "3",
"label": "Procedural"
},
"secondary": {
"code": "2",
"label": "Conceptual"
}
},
"document_type_v1": {
"primary": {
"code": "5",
"label": "Social/Forum"
},
"secondary": {
"code": "3",
"label": "Reference/Encyclopedic/Educational"
}
},
"extraction_artifacts": {
"primary": {
"code": "0",
"label": "No Artifacts"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"missing_content": {
"primary": {
"code": "0",
"label": "No missing content"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"document_type_v2": {
"primary": {
"code": "18",
"label": "Q&A Forum"
},
"secondary": {
"code": "21",
"label": "Customer Support"
}
},
"reasoning_depth": {
"primary": {
"code": "3",
"label": "Intermediate Reasoning"
},
"secondary": {
"code": "2",
"label": "Basic Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "3",
"label": "Mostly Correct"
},
"secondary": {
"code": "4",
"label": "Highly Correct"
}
},
"education_level": {
"primary": {
"code": "3",
"label": "Undergraduate Level"
},
"secondary": {
"code": "2",
"label": "High School Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
8,822,321,820,984,856,000 |
< prev index next >
src/hotspot/cpu/x86/stubGenerator_x86_64.cpp
Print this page
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
18 *
19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 *
23 */
24
25 #include "precompiled.hpp"
26 #include "asm/macroAssembler.hpp"
27 #include "classfile/vmIntrinsics.hpp"
28 #include "compiler/oopMap.hpp"
29 #include "gc/shared/barrierSet.hpp"
30 #include "gc/shared/barrierSetAssembler.hpp"
31 #include "gc/shared/barrierSetNMethod.hpp"
32 #include "gc/shared/gc_globals.hpp"
33 #include "memory/universe.hpp"
34 #include "prims/jvmtiExport.hpp"
35 #include "prims/upcallLinker.hpp"
36 #include "runtime/arguments.hpp"
37 #include "runtime/javaThread.hpp"
38 #include "runtime/sharedRuntime.hpp"
39 #include "runtime/stubRoutines.hpp"
40 #include "stubGenerator_x86_64.hpp"
41 #ifdef COMPILER2
42 #include "opto/runtime.hpp"
43 #include "opto/c2_globals.hpp"
44 #endif
45 #if INCLUDE_JVMCI
46 #include "jvmci/jvmci_globals.hpp"
47 #endif
48 #if INCLUDE_JFR
49 #include "jfr/support/jfrIntrinsics.hpp"
50 #endif
51
52 // For a more detailed description of the stub routine structure
53 // see the comment in stubRoutines.hpp
54
55 #define __ _masm->
56 #define TIMES_OOP (UseCompressedOops ? Address::times_4 : Address::times_8)
3767 __ ret(0);
3768 }
3769
3770 return start;
3771 }
3772
3773 address StubGenerator::generate_cont_thaw() {
3774 return generate_cont_thaw("Cont thaw", Continuation::thaw_top);
3775 }
3776
3777 // TODO: will probably need multiple return barriers depending on return type
3778
3779 address StubGenerator::generate_cont_returnBarrier() {
3780 return generate_cont_thaw("Cont thaw return barrier", Continuation::thaw_return_barrier);
3781 }
3782
3783 address StubGenerator::generate_cont_returnBarrier_exception() {
3784 return generate_cont_thaw("Cont thaw return barrier exception", Continuation::thaw_return_barrier_exception);
3785 }
3786
3787 #if INCLUDE_JFR
3788
3789 // For c2: c_rarg0 is junk, call to runtime to write a checkpoint.
3790 // It returns a jobject handle to the event writer.
3791 // The handle is dereferenced and the return value is the event writer oop.
3792 RuntimeStub* StubGenerator::generate_jfr_write_checkpoint() {
3793 enum layout {
3794 rbp_off,
3795 rbpH_off,
3796 return_off,
3797 return_off2,
3798 framesize // inclusive of return address
3799 };
3800
3801 CodeBuffer code("jfr_write_checkpoint", 1024, 64);
3802 MacroAssembler* _masm = new MacroAssembler(&code);
3803 address start = __ pc();
3804
3805 __ enter();
3806 address the_pc = __ pc();
4072 if (VM_Version::supports_float16()) {
4073 // For results consistency both intrinsics should be enabled.
4074 // vmIntrinsics checks InlineIntrinsics flag, no need to check it here.
4075 if (vmIntrinsics::is_intrinsic_available(vmIntrinsics::_float16ToFloat) &&
4076 vmIntrinsics::is_intrinsic_available(vmIntrinsics::_floatToFloat16)) {
4077 StubRoutines::_hf2f = generate_float16ToFloat();
4078 StubRoutines::_f2hf = generate_floatToFloat16();
4079 }
4080 }
4081
4082 generate_libm_stubs();
4083
4084 StubRoutines::_fmod = generate_libmFmod(); // from stubGenerator_x86_64_fmod.cpp
4085 }
4086
4087 void StubGenerator::generate_continuation_stubs() {
4088 // Continuation stubs:
4089 StubRoutines::_cont_thaw = generate_cont_thaw();
4090 StubRoutines::_cont_returnBarrier = generate_cont_returnBarrier();
4091 StubRoutines::_cont_returnBarrierExc = generate_cont_returnBarrier_exception();
4092
4093 JFR_ONLY(generate_jfr_stubs();)
4094 }
4095
4096 #if INCLUDE_JFR
4097 void StubGenerator::generate_jfr_stubs() {
4098 StubRoutines::_jfr_write_checkpoint_stub = generate_jfr_write_checkpoint();
4099 StubRoutines::_jfr_write_checkpoint = StubRoutines::_jfr_write_checkpoint_stub->entry_point();
4100 StubRoutines::_jfr_return_lease_stub = generate_jfr_return_lease();
4101 StubRoutines::_jfr_return_lease = StubRoutines::_jfr_return_lease_stub->entry_point();
4102 }
4103 #endif
4104
4105 void StubGenerator::generate_final_stubs() {
4106 // Generates the rest of stubs and initializes the entry points
4107
4108 // These entry points require SharedInfo::stack0 to be set up in
4109 // non-core builds and need to be relocatable, so they each
4110 // fabricate a RuntimeStub internally.
4111 StubRoutines::_throw_AbstractMethodError_entry =
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
18 *
19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 *
23 */
24
25 #include "precompiled.hpp"
26 #include "asm/macroAssembler.hpp"
27 #include "classfile/vmIntrinsics.hpp"
28 #include "compiler/oopMap.hpp"
29 #include "gc/shared/barrierSet.hpp"
30 #include "gc/shared/barrierSetAssembler.hpp"
31 #include "gc/shared/barrierSetNMethod.hpp"
32 #include "gc/shared/gc_globals.hpp"
33 #include "memory/universe.hpp"
34 #include "prims/jvmtiExport.hpp"
35 #include "prims/upcallLinker.hpp"
36 #include "runtime/arguments.hpp"
37 #include "runtime/continuationEntry.hpp"
38 #include "runtime/javaThread.hpp"
39 #include "runtime/sharedRuntime.hpp"
40 #include "runtime/stubRoutines.hpp"
41 #include "stubGenerator_x86_64.hpp"
42 #ifdef COMPILER2
43 #include "opto/runtime.hpp"
44 #include "opto/c2_globals.hpp"
45 #endif
46 #if INCLUDE_JVMCI
47 #include "jvmci/jvmci_globals.hpp"
48 #endif
49 #if INCLUDE_JFR
50 #include "jfr/support/jfrIntrinsics.hpp"
51 #endif
52
53 // For a more detailed description of the stub routine structure
54 // see the comment in stubRoutines.hpp
55
56 #define __ _masm->
57 #define TIMES_OOP (UseCompressedOops ? Address::times_4 : Address::times_8)
3768 __ ret(0);
3769 }
3770
3771 return start;
3772 }
3773
3774 address StubGenerator::generate_cont_thaw() {
3775 return generate_cont_thaw("Cont thaw", Continuation::thaw_top);
3776 }
3777
3778 // TODO: will probably need multiple return barriers depending on return type
3779
3780 address StubGenerator::generate_cont_returnBarrier() {
3781 return generate_cont_thaw("Cont thaw return barrier", Continuation::thaw_return_barrier);
3782 }
3783
3784 address StubGenerator::generate_cont_returnBarrier_exception() {
3785 return generate_cont_thaw("Cont thaw return barrier exception", Continuation::thaw_return_barrier_exception);
3786 }
3787
3788 address StubGenerator::generate_cont_preempt_stub() {
3789 if (!Continuations::enabled()) return nullptr;
3790 StubCodeMark mark(this, "StubRoutines","Continuation preempt stub");
3791 address start = __ pc();
3792
3793 #ifdef ASSERT
3794 __ push(rax);
3795 { Label L;
3796 __ get_thread(rax);
3797 __ cmpptr(r15_thread, rax);
3798 __ jcc(Assembler::equal, L);
3799 __ stop("r15 should have been preserved across VM call");
3800 __ bind(L);
3801 }
3802 __ pop(rax);
3803 #endif
3804
3805 __ reset_last_Java_frame(true);
3806
3807 // reset _preempting flag
3808 #ifdef ASSERT
3809 { Label L;
3810 __ movbool(rscratch1, Address(r15_thread, JavaThread::preempting_offset()));
3811 __ testbool(rscratch1);
3812 __ jcc(Assembler::notZero, L);
3813 __ stop("preempting flag should be set");
3814 __ bind(L);
3815 }
3816 #endif
3817 __ movbool(Address(r15_thread, JavaThread::preempting_offset()), false);
3818
3819 // Set rsp to enterSpecial frame
3820 __ movptr(rsp, Address(r15_thread, JavaThread::cont_entry_offset()));
3821
3822 Label preemption_cancelled;
3823 __ movbool(rscratch1, Address(r15_thread, JavaThread::preemption_cancelled_offset()));
3824 __ testbool(rscratch1);
3825 __ jcc(Assembler::notZero, preemption_cancelled);
3826
3827 // Remove enterSpecial frame from the stack and return to Continuation.run()
3828 SharedRuntime::continuation_enter_cleanup(_masm);
3829 __ pop(rbp);
3830 __ ret(0);
3831
3832 __ bind(preemption_cancelled);
3833 __ lea(rbp, Address(rsp, checked_cast<int32_t>(ContinuationEntry::size())));
3834 __ movptr(rscratch1, ExternalAddress((address)&ContinuationEntry::_thaw_call_pc));
3835 __ jmp(rscratch1);
3836
3837 return start;
3838 }
3839
3840 address StubGenerator::generate_cont_preempt_monitorenter_redo() {
3841 if (!Continuations::enabled()) return nullptr;
3842 StubCodeMark mark(this, "StubRoutines","Continuation monitorenter redo stub");
3843 address start = __ pc();
3844
3845 #ifdef ASSERT
3846 __ push(rax);
3847 { Label L;
3848 __ get_thread(rax);
3849 __ cmpptr(r15_thread, rax);
3850 __ jcc(Assembler::equal, L);
3851 __ stop("r15 should have been preserved across VM call");
3852 __ bind(L);
3853 }
3854 __ pop(rax);
3855 #endif
3856
3857 const Register mon_reg = c_rarg1;
3858 __ pop(mon_reg);
3859 __ pop(mon_reg);
3860
3861 #ifdef ASSERT
3862 { Label L;
3863 __ testptr(mon_reg, mon_reg);
3864 __ jcc(Assembler::notEqual, L);
3865 __ stop("ObjectMonitor to use is null");
3866 __ bind(L);
3867 }
3868 #endif // ASSERT
3869
3870 __ mov(c_rarg0, r15_thread);
3871 __ subptr(rsp, frame::arg_reg_save_area_bytes);
3872 __ call(RuntimeAddress(CAST_FROM_FN_PTR(address, SharedRuntime::redo_monitorenter)));
3873 __ addptr(rsp, frame::arg_reg_save_area_bytes);
3874
3875 Label failAcquire;
3876 __ movbool(rscratch1, Address(r15_thread, JavaThread::preempting_offset()));
3877 __ testbool(rscratch1);
3878 __ jcc(Assembler::notEqual, failAcquire);
3879 // We have the lock now, just return to caller (we will actually hit the
3880 // return barrier to thaw more frames)
3881 __ pop(rbp);
3882 __ ret(0);
3883
3884 __ bind(failAcquire);
3885 __ movbool(Address(r15_thread, JavaThread::preempting_offset()), false);
3886 // Set rsp to enterSpecial frame
3887 __ movptr(rsp, Address(r15_thread, JavaThread::cont_entry_offset()));
3888 // Remove enterSpecial frame from the stack and return to Continuation.run()
3889 SharedRuntime::continuation_enter_cleanup(_masm);
3890 __ pop(rbp);
3891 __ ret(0);
3892
3893 return start;
3894 }
3895
3896 address StubGenerator::generate_cont_preempt_rerun_compiler_adapter() {
3897 if (!Continuations::enabled()) return nullptr;
3898 StubCodeMark mark(this, "StubRoutines", "Continuation preempt safepoint blob adapter");
3899 address start = __ pc();
3900
3901 // The safepoint blob handler expects that rbx, being a callee saved register, will be preserved
3902 // during the VM call. It is used to check if the return pc back to Java was modified in the runtime.
3903 // If it wasn't, the return pc is modified so on return the poll instruction is skipped. Saving this
3904 // additional value of rbx during freeze will complicate too much the code, so we just zero it here
3905 // so that the comparison fails and the skip is not attempted in case the pc was indeed changed.
3906 __ movptr(rbx, NULL_WORD);
3907
3908 __ pop(rbp);
3909 __ ret(0);
3910
3911 return start;
3912 }
3913
3914 #if INCLUDE_JFR
3915
3916 // For c2: c_rarg0 is junk, call to runtime to write a checkpoint.
3917 // It returns a jobject handle to the event writer.
3918 // The handle is dereferenced and the return value is the event writer oop.
3919 RuntimeStub* StubGenerator::generate_jfr_write_checkpoint() {
3920 enum layout {
3921 rbp_off,
3922 rbpH_off,
3923 return_off,
3924 return_off2,
3925 framesize // inclusive of return address
3926 };
3927
3928 CodeBuffer code("jfr_write_checkpoint", 1024, 64);
3929 MacroAssembler* _masm = new MacroAssembler(&code);
3930 address start = __ pc();
3931
3932 __ enter();
3933 address the_pc = __ pc();
4199 if (VM_Version::supports_float16()) {
4200 // For results consistency both intrinsics should be enabled.
4201 // vmIntrinsics checks InlineIntrinsics flag, no need to check it here.
4202 if (vmIntrinsics::is_intrinsic_available(vmIntrinsics::_float16ToFloat) &&
4203 vmIntrinsics::is_intrinsic_available(vmIntrinsics::_floatToFloat16)) {
4204 StubRoutines::_hf2f = generate_float16ToFloat();
4205 StubRoutines::_f2hf = generate_floatToFloat16();
4206 }
4207 }
4208
4209 generate_libm_stubs();
4210
4211 StubRoutines::_fmod = generate_libmFmod(); // from stubGenerator_x86_64_fmod.cpp
4212 }
4213
4214 void StubGenerator::generate_continuation_stubs() {
4215 // Continuation stubs:
4216 StubRoutines::_cont_thaw = generate_cont_thaw();
4217 StubRoutines::_cont_returnBarrier = generate_cont_returnBarrier();
4218 StubRoutines::_cont_returnBarrierExc = generate_cont_returnBarrier_exception();
4219 StubRoutines::_cont_preempt_stub = generate_cont_preempt_stub();
4220 StubRoutines::_cont_preempt_monitorenter_redo = generate_cont_preempt_monitorenter_redo();
4221 StubRoutines::_cont_preempt_rerun_compiler_adapter = generate_cont_preempt_rerun_compiler_adapter();
4222
4223 JFR_ONLY(generate_jfr_stubs();)
4224 }
4225
4226 #if INCLUDE_JFR
4227 void StubGenerator::generate_jfr_stubs() {
4228 StubRoutines::_jfr_write_checkpoint_stub = generate_jfr_write_checkpoint();
4229 StubRoutines::_jfr_write_checkpoint = StubRoutines::_jfr_write_checkpoint_stub->entry_point();
4230 StubRoutines::_jfr_return_lease_stub = generate_jfr_return_lease();
4231 StubRoutines::_jfr_return_lease = StubRoutines::_jfr_return_lease_stub->entry_point();
4232 }
4233 #endif
4234
4235 void StubGenerator::generate_final_stubs() {
4236 // Generates the rest of stubs and initializes the entry points
4237
4238 // These entry points require SharedInfo::stack0 to be set up in
4239 // non-core builds and need to be relocatable, so they each
4240 // fabricate a RuntimeStub internally.
4241 StubRoutines::_throw_AbstractMethodError_entry =
< prev index next >
|
{
"url": "https://builds.shipilev.net/patch-openjdk-loom-fibers/src/hotspot/cpu/x86/stubGenerator_x86_64.cpp.sdiff.html",
"source_domain": "builds.shipilev.net",
"snapshot_id": "CC-MAIN-2024-10",
"warc_metadata": {
"Content-Length": "20317",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:JN42W2V57QR3WW3EVEYFXLQEH5LLJWKY",
"WARC-Concurrent-To": "<urn:uuid:a6080501-a15f-43f4-8d24-3495de9c8ddb>",
"WARC-Date": "2024-02-24T12:12:03Z",
"WARC-IP-Address": "144.76.31.130",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:JN4EGK5S4TS44323KLGBU4YLVNPHICEL",
"WARC-Record-ID": "<urn:uuid:58584a4d-16aa-46c6-99f3-0a0f013c4ea0>",
"WARC-Target-URI": "https://builds.shipilev.net/patch-openjdk-loom-fibers/src/hotspot/cpu/x86/stubGenerator_x86_64.cpp.sdiff.html",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:033f3c1f-90ef-4597-82c6-90bdb34a68aa>"
},
"warc_info": "isPartOf: CC-MAIN-2024-10\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for February/March 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-196\r\nsoftware: Apache Nutch 1.19 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
20,
21,
66,
67,
83,
84,
154,
162,
242,
321,
340,
348,
357,
363,
395,
434,
477,
513,
554,
604,
652,
693,
729,
767,
806,
844,
845,
884,
926,
967,
1008,
1030,
1063,
1099,
1111,
1134,
1174,
1186,
1207,
1253,
1265,
1271,
1341,
1385,
1391,
1415,
1496,
1497,
1517,
1526,
1532,
1553,
1560,
1566,
1617,
1688,
1695,
1701,
1784,
1790,
1850,
1947,
1954,
1960,
2030,
2147,
2154,
2160,
2161,
2162,
2163,
2164,
2165,
2166,
2167,
2168,
2169,
2170,
2171,
2172,
2173,
2174,
2175,
2176,
2177,
2178,
2179,
2180,
2181,
2182,
2183,
2184,
2185,
2186,
2187,
2188,
2189,
2190,
2191,
2192,
2193,
2194,
2195,
2196,
2197,
2198,
2199,
2200,
2201,
2202,
2203,
2204,
2205,
2206,
2207,
2208,
2209,
2210,
2211,
2212,
2213,
2214,
2215,
2216,
2217,
2218,
2219,
2220,
2221,
2222,
2223,
2224,
2225,
2226,
2227,
2228,
2229,
2230,
2231,
2232,
2233,
2234,
2235,
2236,
2237,
2238,
2239,
2240,
2241,
2242,
2243,
2244,
2245,
2246,
2247,
2248,
2249,
2250,
2251,
2252,
2253,
2254,
2255,
2256,
2257,
2258,
2259,
2260,
2261,
2262,
2263,
2264,
2265,
2266,
2267,
2268,
2269,
2270,
2271,
2272,
2273,
2274,
2275,
2276,
2277,
2278,
2279,
2280,
2281,
2282,
2283,
2284,
2285,
2286,
2307,
2313,
2385,
2442,
2523,
2590,
2611,
2629,
2648,
2669,
2691,
2741,
2751,
2757,
2815,
2873,
2905,
2911,
2930,
2963,
2964,
3009,
3080,
3161,
3245,
3329,
3389,
3449,
3460,
3469,
3475,
3505,
3511,
3599,
3606,
3612,
3669,
3699,
3764,
3838,
3925,
3926,
3927,
3928,
3934,
3973,
3980,
3986,
4007,
4055,
4138,
4240,
4315,
4409,
4416,
4428,
4434,
4484,
4555,
4561,
4633,
4700,
4746,
4802,
4803,
4873,
4881,
4961,
5040,
5059,
5067,
5076,
5082,
5114,
5153,
5196,
5232,
5273,
5323,
5371,
5412,
5448,
5486,
5525,
5563,
5609,
5648,
5690,
5731,
5772,
5794,
5827,
5863,
5875,
5898,
5938,
5950,
5971,
6017,
6029,
6035,
6105,
6149,
6155,
6179,
6260,
6261,
6281,
6290,
6296,
6317,
6324,
6330,
6381,
6452,
6459,
6465,
6548,
6554,
6614,
6711,
6718,
6724,
6794,
6911,
6918,
6924,
6983,
7037,
7113,
7145,
7151,
7170,
7191,
7209,
7238,
7275,
7313,
7380,
7401,
7410,
7430,
7442,
7448,
7487,
7493,
7526,
7545,
7563,
7649,
7682,
7722,
7773,
7794,
7803,
7815,
7895,
7901,
7941,
8018,
8024,
8059,
8153,
8184,
8241,
8247,
8331,
8388,
8408,
8426,
8432,
8470,
8554,
8644,
8670,
8676,
8697,
8704,
8710,
8782,
8836,
8922,
8954,
8960,
8979,
9000,
9018,
9047,
9084,
9122,
9189,
9210,
9219,
9239,
9251,
9257,
9298,
9322,
9346,
9352,
9371,
9389,
9428,
9469,
9519,
9540,
9549,
9571,
9577,
9613,
9668,
9761,
9816,
9822,
9848,
9932,
9963,
10012,
10092,
10138,
10158,
10176,
10182,
10211,
10291,
10331,
10408,
10492,
10549,
10569,
10587,
10593,
10614,
10621,
10627,
10704,
10758,
10853,
10885,
10891,
10995,
11104,
11212,
11319,
11423,
11457,
11463,
11483,
11501,
11507,
11528,
11535,
11541,
11562,
11568,
11640,
11697,
11778,
11845,
11866,
11884,
11903,
11924,
11946,
11996,
12006,
12012,
12070,
12128,
12160,
12166,
12185,
12218,
12219,
12264,
12335,
12416,
12500,
12584,
12644,
12704,
12715,
12724,
12730,
12760,
12766,
12854,
12861,
12867,
12924,
12954,
13019,
13093,
13180,
13252,
13350,
13458,
13464,
13503,
13510,
13516,
13537,
13585,
13668,
13770,
13845,
13939,
13946,
13958,
13964,
14014,
14085,
14091,
14163,
14230,
14276,
14332
],
"line_end_idx": [
20,
21,
66,
67,
83,
84,
154,
162,
242,
321,
340,
348,
357,
363,
395,
434,
477,
513,
554,
604,
652,
693,
729,
767,
806,
844,
845,
884,
926,
967,
1008,
1030,
1063,
1099,
1111,
1134,
1174,
1186,
1207,
1253,
1265,
1271,
1341,
1385,
1391,
1415,
1496,
1497,
1517,
1526,
1532,
1553,
1560,
1566,
1617,
1688,
1695,
1701,
1784,
1790,
1850,
1947,
1954,
1960,
2030,
2147,
2154,
2160,
2161,
2162,
2163,
2164,
2165,
2166,
2167,
2168,
2169,
2170,
2171,
2172,
2173,
2174,
2175,
2176,
2177,
2178,
2179,
2180,
2181,
2182,
2183,
2184,
2185,
2186,
2187,
2188,
2189,
2190,
2191,
2192,
2193,
2194,
2195,
2196,
2197,
2198,
2199,
2200,
2201,
2202,
2203,
2204,
2205,
2206,
2207,
2208,
2209,
2210,
2211,
2212,
2213,
2214,
2215,
2216,
2217,
2218,
2219,
2220,
2221,
2222,
2223,
2224,
2225,
2226,
2227,
2228,
2229,
2230,
2231,
2232,
2233,
2234,
2235,
2236,
2237,
2238,
2239,
2240,
2241,
2242,
2243,
2244,
2245,
2246,
2247,
2248,
2249,
2250,
2251,
2252,
2253,
2254,
2255,
2256,
2257,
2258,
2259,
2260,
2261,
2262,
2263,
2264,
2265,
2266,
2267,
2268,
2269,
2270,
2271,
2272,
2273,
2274,
2275,
2276,
2277,
2278,
2279,
2280,
2281,
2282,
2283,
2284,
2285,
2286,
2307,
2313,
2385,
2442,
2523,
2590,
2611,
2629,
2648,
2669,
2691,
2741,
2751,
2757,
2815,
2873,
2905,
2911,
2930,
2963,
2964,
3009,
3080,
3161,
3245,
3329,
3389,
3449,
3460,
3469,
3475,
3505,
3511,
3599,
3606,
3612,
3669,
3699,
3764,
3838,
3925,
3926,
3927,
3928,
3934,
3973,
3980,
3986,
4007,
4055,
4138,
4240,
4315,
4409,
4416,
4428,
4434,
4484,
4555,
4561,
4633,
4700,
4746,
4802,
4803,
4873,
4881,
4961,
5040,
5059,
5067,
5076,
5082,
5114,
5153,
5196,
5232,
5273,
5323,
5371,
5412,
5448,
5486,
5525,
5563,
5609,
5648,
5690,
5731,
5772,
5794,
5827,
5863,
5875,
5898,
5938,
5950,
5971,
6017,
6029,
6035,
6105,
6149,
6155,
6179,
6260,
6261,
6281,
6290,
6296,
6317,
6324,
6330,
6381,
6452,
6459,
6465,
6548,
6554,
6614,
6711,
6718,
6724,
6794,
6911,
6918,
6924,
6983,
7037,
7113,
7145,
7151,
7170,
7191,
7209,
7238,
7275,
7313,
7380,
7401,
7410,
7430,
7442,
7448,
7487,
7493,
7526,
7545,
7563,
7649,
7682,
7722,
7773,
7794,
7803,
7815,
7895,
7901,
7941,
8018,
8024,
8059,
8153,
8184,
8241,
8247,
8331,
8388,
8408,
8426,
8432,
8470,
8554,
8644,
8670,
8676,
8697,
8704,
8710,
8782,
8836,
8922,
8954,
8960,
8979,
9000,
9018,
9047,
9084,
9122,
9189,
9210,
9219,
9239,
9251,
9257,
9298,
9322,
9346,
9352,
9371,
9389,
9428,
9469,
9519,
9540,
9549,
9571,
9577,
9613,
9668,
9761,
9816,
9822,
9848,
9932,
9963,
10012,
10092,
10138,
10158,
10176,
10182,
10211,
10291,
10331,
10408,
10492,
10549,
10569,
10587,
10593,
10614,
10621,
10627,
10704,
10758,
10853,
10885,
10891,
10995,
11104,
11212,
11319,
11423,
11457,
11463,
11483,
11501,
11507,
11528,
11535,
11541,
11562,
11568,
11640,
11697,
11778,
11845,
11866,
11884,
11903,
11924,
11946,
11996,
12006,
12012,
12070,
12128,
12160,
12166,
12185,
12218,
12219,
12264,
12335,
12416,
12500,
12584,
12644,
12704,
12715,
12724,
12730,
12760,
12766,
12854,
12861,
12867,
12924,
12954,
13019,
13093,
13180,
13252,
13350,
13458,
13464,
13503,
13510,
13516,
13537,
13585,
13668,
13770,
13845,
13939,
13946,
13958,
13964,
14014,
14085,
14091,
14163,
14230,
14276,
14332,
14351
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 14351,
"ccnet_original_nlines": 511,
"rps_doc_curly_bracket": 0.0039021701086312532,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.08736348897218704,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.017940720543265343,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.5230109095573425,
"rps_doc_frac_unique_words": 0.4768413007259369,
"rps_doc_mean_word_length": 7.751708507537842,
"rps_doc_num_sentences": 80,
"rps_doc_symbol_to_word_ratio": 0.027691109105944633,
"rps_doc_unigram_entropy": 6.0640435218811035,
"rps_doc_word_count": 1317,
"rps_doc_frac_chars_dupe_10grams": 0.2033499926328659,
"rps_doc_frac_chars_dupe_5grams": 0.28915661573410034,
"rps_doc_frac_chars_dupe_6grams": 0.26075032353401184,
"rps_doc_frac_chars_dupe_7grams": 0.24664512276649475,
"rps_doc_frac_chars_dupe_8grams": 0.2290136218070984,
"rps_doc_frac_chars_dupe_9grams": 0.2109902948141098,
"rps_doc_frac_chars_top_2gram": 0.007052599918097258,
"rps_doc_frac_chars_top_3gram": 0.017631499096751213,
"rps_doc_frac_chars_top_4gram": 0.014105199836194515,
"rps_doc_books_importance": -1153.9754638671875,
"rps_doc_books_importance_length_correction": -1153.9754638671875,
"rps_doc_openwebtext_importance": -667.7225341796875,
"rps_doc_openwebtext_importance_length_correction": -667.7225341796875,
"rps_doc_wikipedia_importance": -617.7850952148438,
"rps_doc_wikipedia_importance_length_correction": -617.7850952148438
},
"fasttext": {
"dclm": 0.621929407119751,
"english": 0.3914494812488556,
"fineweb_edu_approx": 1.9639601707458496,
"eai_general_math": 0.6188485622406006,
"eai_open_web_math": 0.11459916830062866,
"eai_web_code": 0.061716679483652115
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "005.4",
"labels": {
"level_1": "General works, books and libraries, information sciences",
"level_2": "",
"level_3": "Computer programming"
}
},
"secondary": {
"code": "004.27",
"labels": {
"level_1": "General works, books and libraries, information sciences",
"level_2": "",
"level_3": "Computers and Computer science"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "3",
"label": "Apply"
},
"secondary": {
"code": "4",
"label": "Analyze"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "3",
"label": "Procedural"
},
"secondary": {
"code": "2",
"label": "Conceptual"
}
},
"document_type_v1": {
"primary": {
"code": "4",
"label": "Code/Software"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "1",
"label": "Leftover HTML"
},
"secondary": {
"code": "3",
"label": "Irrelevant Content"
}
},
"missing_content": {
"primary": {
"code": "0",
"label": "No missing content"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"document_type_v2": {
"primary": {
"code": "8",
"label": "Documentation"
},
"secondary": {
"code": "3",
"label": "Academic Writing"
}
},
"reasoning_depth": {
"primary": {
"code": "4",
"label": "Advanced Reasoning"
},
"secondary": {
"code": "3",
"label": "Intermediate Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "4",
"label": "Highly Correct"
},
"secondary": {
"code": "3",
"label": "Mostly Correct"
}
},
"education_level": {
"primary": {
"code": "4",
"label": "Graduate/Expert Level"
},
"secondary": {
"code": "3",
"label": "Undergraduate Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
2,070,308,879,048,077,600 |
The Most Common Hereditary Health Issues
hereditary
There are a number of hereditary health issues that can affect a person throughout their life that they can inherit from their parents. Some of these issues are more serious than others, but all can impact the quality of life for the person affected and their family. These types of health issues can be passed down from generation to generation, and often run in families. If you know that you or your family members have a history of a particular health issue, it is crucial to be aware of the risks and to take steps to reduce your risk of developing the condition. If you want to learn more, keep reading to find out about the most common hereditary health issues.
What are the most common hereditary health issues?
Mental illness is one of the most common hereditary health issues that can be passed on. It can be passed down from one generation to the next. Some mental illnesses are caused by a combination of genes and environmental factors. However, many mental illnesses are caused by a single gene. If one or both of your parents have a mental illness, you are at a higher risk of developing a mental illness. If you think you might be at risk for developing a hereditary mental health condition, you should talk to a therapist as soon as possible.
When it comes to genetic diseases, cystic fibrosis is one of the most widespread. If you have cystic fibrosis, you’ll likely need to see a respiratory therapist. Respiratory therapists are trained professionals who have received a masters in respiratory therapy. Respiratory therapy is an important part of the treatment for patients with cystic fibrosis. The therapist can clear the patient’s lungs, which will allow them to breathe more easily. The therapist may use a machine to help the patient breathe, called a ventilator. The therapist can also teach the patient exercises to clear the lungs.
See also Filing a Medical Malpractice Lawsuit
Genetic testing can help you find out if you have hereditary health issues. This type of testing can identify changes in your DNA that may increase your risk of developing a particular disease or condition. You can even upload raw DNA data from any genetic testing website and learn even more about your genetic potential.
hereditary
How can you take better care of yourself?
Lack of sleep can have a significant effect on your health and wellness. Lack of sleep can lead to health problems, including obesity, heart disease, and diabetes. It can also lead to decreased productivity and mood swings. If you’re having trouble sleeping, there are a few things you can do to help. For one, make sure you’re sleeping in a dark, quiet room. Avoid using electronic devices before bed, and invest in a comfortable mattress. You may also want to try some relaxation techniques, such as deep breathing or yoga. If these methods don’t work, talk to your doctor about possible sleep disorders.
Exercise has been shown to improve mental health, assist with weight loss or maintenance, increase lifespan, lower blood pressure, and reduce the risk of chronic diseases. Research indicates that, from a mental health perspective, exercise can boost mood, decrease anxiety and stress, and improve sleep quality. Working out regularly also reduces your blood pressure and lowers your risk of developing certain types of chronic diseases, like heart disease. If you’re new to physical fitness, start slowly and work up to a more rigorous routine.
As you can see, there is a lot to learn about hereditary health issues. Overall, you need to educate yourself about any potential hereditary health issues you may have. They can affect a person’s quality of life and health in many ways, and in some cases, they can be life-threatening. It is necessary to be aware of the symptoms of common conditions and to talk to a doctor if you have any concerns. You should also make it a priority to take care of yourself on a daily basis by getting plenty of rest, maintaining a healthy diet, and getting regular exercise. If you follow these tips, you’ll be able to protect your health and get treatment for any health conditions you may have as soon as possible.
See also Piercing Aftercare Guide for Fast and Easy Healing
|
{
"url": "https://www.healthtipslive.com/the-most-common-hereditary-health-issues/",
"source_domain": "www.healthtipslive.com",
"snapshot_id": "CC-MAIN-2024-26",
"warc_metadata": {
"Content-Length": "54364",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:OBCKBOAGGT5KUX3SZF3ASYORDSNYNNVZ",
"WARC-Concurrent-To": "<urn:uuid:80f638e1-9f1d-43ba-a74a-aa44bfa476cf>",
"WARC-Date": "2024-06-14T09:56:48Z",
"WARC-IP-Address": "209.59.169.250",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:5LD62XIFLK62DTLYE76YFIZD7YYHDMI6",
"WARC-Record-ID": "<urn:uuid:03530e9c-7bcc-43a4-8627-348476fe3582>",
"WARC-Target-URI": "https://www.healthtipslive.com/the-most-common-hereditary-health-issues/",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:dc6bcc17-1ede-429f-9c3d-23ff07150a97>"
},
"warc_info": "isPartOf: CC-MAIN-2024-26\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for June 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-80\r\nsoftware: Apache Nutch 1.20 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
41,
42,
53,
54,
723,
724,
775,
776,
1316,
1317,
1917,
1918,
1966,
1967,
2290,
2291,
2302,
2303,
2345,
2346,
2953,
2954,
3499,
3500,
4205,
4206
],
"line_end_idx": [
41,
42,
53,
54,
723,
724,
775,
776,
1316,
1317,
1917,
1918,
1966,
1967,
2290,
2291,
2302,
2303,
2345,
2346,
2953,
2954,
3499,
3500,
4205,
4206,
4266
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 4266,
"ccnet_original_nlines": 26,
"rps_doc_curly_bracket": 0,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.46585366129875183,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.0024390199687331915,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.10609755665063858,
"rps_doc_frac_unique_words": 0.3922652006149292,
"rps_doc_mean_word_length": 4.7624311447143555,
"rps_doc_num_sentences": 42,
"rps_doc_symbol_to_word_ratio": 0,
"rps_doc_unigram_entropy": 5.066806793212891,
"rps_doc_word_count": 724,
"rps_doc_frac_chars_dupe_10grams": 0,
"rps_doc_frac_chars_dupe_5grams": 0.0919373482465744,
"rps_doc_frac_chars_dupe_6grams": 0.05626450106501579,
"rps_doc_frac_chars_dupe_7grams": 0,
"rps_doc_frac_chars_dupe_8grams": 0,
"rps_doc_frac_chars_dupe_9grams": 0,
"rps_doc_frac_chars_top_2gram": 0.03132250905036926,
"rps_doc_frac_chars_top_3gram": 0.05104408040642738,
"rps_doc_frac_chars_top_4gram": 0.026682129129767418,
"rps_doc_books_importance": -367.2752685546875,
"rps_doc_books_importance_length_correction": -367.2752685546875,
"rps_doc_openwebtext_importance": -224.983642578125,
"rps_doc_openwebtext_importance_length_correction": -224.983642578125,
"rps_doc_wikipedia_importance": -122.13946533203125,
"rps_doc_wikipedia_importance_length_correction": -122.13946533203125
},
"fasttext": {
"dclm": 0.04145228862762451,
"english": 0.9597193002700806,
"fineweb_edu_approx": 3.0072429180145264,
"eai_general_math": 0.02828890085220337,
"eai_open_web_math": 0.14741605520248413,
"eai_web_code": 0.0011261700419709086
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "616.042",
"labels": {
"level_1": "Industrial arts, Technology, and Engineering",
"level_2": "Medicine",
"level_3": "Pathology and Diseases"
}
},
"secondary": {
"code": "616.85",
"labels": {
"level_1": "Industrial arts, Technology, and Engineering",
"level_2": "Medicine",
"level_3": "Pathology and Diseases"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "2",
"label": "Understand"
},
"secondary": {
"code": "3",
"label": "Apply"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "2",
"label": "Conceptual"
},
"secondary": {
"code": "3",
"label": "Procedural"
}
},
"document_type_v1": {
"primary": {
"code": "3",
"label": "Reference/Encyclopedic/Educational"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "3",
"label": "Irrelevant Content"
},
"secondary": {
"code": "0",
"label": "No Artifacts"
}
},
"missing_content": {
"primary": {
"code": "4",
"label": "Missing Images or Figures"
},
"secondary": {
"code": "0",
"label": "No missing content"
}
},
"document_type_v2": {
"primary": {
"code": "10",
"label": "Knowledge Article"
},
"secondary": {
"code": "12",
"label": "Listicle"
}
},
"reasoning_depth": {
"primary": {
"code": "2",
"label": "Basic Reasoning"
},
"secondary": {
"code": "1",
"label": "No Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "3",
"label": "Mostly Correct"
},
"secondary": {
"code": "6",
"label": "Not Applicable/Indeterminate"
}
},
"education_level": {
"primary": {
"code": "1",
"label": "General Audience"
},
"secondary": {
"code": "2",
"label": "High School Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
5,006,949,593,540,895,000 |
Unlocking the Mystery: How Many Beers Does It Take to Get Drunk?
Curious about the magic number of beers needed to feel tipsy? Discover the mystery behind alcohol tolerance and intoxication levels.
Crop anonymous male partners with glass bottles of alcoholic drink sitting at wooden table on weekend
Image courtesy of Anete Lusina via Pexels
How Many Beers to Get Drunk: Exploring the Science Behind Alcohol Intoxication
Alcohol consumption is a common social activity that many people enjoy. However, understanding how alcohol affects our bodies and knowing our personal limits is essential for responsible drinking. One of the most intriguing questions that often arises is how many beers it takes to get drunk. Let’s delve into the science behind alcohol intoxication to shed light on this mystery.
Alcohol Metabolism
When we consume alcohol, our bodies go through a complex process of metabolizing it. Alcohol is primarily broken down in the liver by enzymes. The rate at which alcohol is metabolized can vary depending on several factors, including genetic predispositions, age, gender, and overall health Men generally metabolize alcohol faster than women due to differences in body composition and enzyme activity.
Individual Tolerance Levels
Alcohol tolerance refers to the amount of alcohol a person can consume before feeling intoxicated. Several factors can influence an individual’s alcohol tolerance, such as genetics, body weight, and previous exposure to alcohol. People with a family history of alcoholism may have a lower tolerance for alcohol, while heavier individuals may require more alcohol to feel the same effects. Additionally, regular drinkers tend to develop a higher tolerance over time.
Factors Influencing Intoxication
The number of beers it takes to get drunk can vary depending on several factors. The type of alcohol consumed plays a role in intoxication, with beverages containing higher alcohol content leading to quicker intoxication. Consuming alcohol on an empty stomach can also result in faster absorption and increased intoxication. Mixing alcohol with other substances, such as medication or drugs, can have dangerous effects on the body and intensify intoxication. Furthermore, a person’s mental and emotional state can impact how they respond to alcohol, with stress and anxiety potentially increasing the effects of intoxication.
Image result for Unlocking the Mystery: How Many Beers Does It Take to Get Drunk? infographics
Image courtesy of www.freepik.com via Google Images
Conclusion
Understanding the science behind alcohol metabolism, individual tolerance levels, and factors influencing intoxication is crucial for making informed decisions about alcohol consumption. While the number of beers it takes to get drunk can vary from person to person, being aware of your own limits and practicing responsible drinking can help prevent alcohol-related harm. Remember to always drink in moderation and be mindful of the effects of alcohol on your body and mind.
How does alcohol affect different people?
Alcohol affects individuals differently based on factors like genetics, body weight, and tolerance levels. Some may feel drunk after one beer, while others may require more to reach the same level of intoxication.
Can mixing different types of alcohol impact intoxication levels?
Mixing alcohol can intensify intoxication as different types of alcohol may have varying effects on the body. Combining beverages with higher alcohol content can lead to quicker intoxication and increased impairment.
Is it safe to drink on an empty stomach?
Drinking on an empty stomach can lead to faster alcohol absorption, resulting in quicker intoxication. It’s advisable to eat before consuming alcohol to slow down the absorption process and reduce the risk of alcohol-related harm.
How can one determine their alcohol tolerance?
Alcohol tolerance varies among individuals and can change over time. Pay attention to how your body reacts to alcohol, monitor your consumption, and observe how you feel after drinking to understand your personal tolerance levels. It’s important to drink responsibly and know your limits.
Powered by Texta.ai Blog Automation
Posted
in
,
by
Tags:
Comments
Leave a Reply
Your email address will not be published. Required fields are marked *
|
{
"url": "https://www.recoveryprotocols.com/unlocking-the-mystery-how-many-beers-does-it-take-to-get-drunk/",
"source_domain": "www.recoveryprotocols.com",
"snapshot_id": "CC-MAIN-2024-38",
"warc_metadata": {
"Content-Length": "71745",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:56RRRD53II5ZCSFFOBAKR2KEBPSELTID",
"WARC-Concurrent-To": "<urn:uuid:5ff11fa6-2f66-4d1d-972c-8fa90cb12e2a>",
"WARC-Date": "2024-09-09T02:47:41Z",
"WARC-IP-Address": "104.225.217.233",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:5QKJO453FF6UUNI7CZ2GAYVHYJJHD7UO",
"WARC-Record-ID": "<urn:uuid:c041fdfd-190e-4de2-8714-956db1f33f60>",
"WARC-Target-URI": "https://www.recoveryprotocols.com/unlocking-the-mystery-how-many-beers-does-it-take-to-get-drunk/",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:ab963550-ace9-4a1c-9abf-89ac3a880118>"
},
"warc_info": "isPartOf: CC-MAIN-2024-38\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for September 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-65\r\nsoftware: Apache Nutch 1.20 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
65,
66,
199,
200,
302,
303,
345,
346,
425,
426,
807,
808,
827,
828,
1229,
1230,
1258,
1259,
1725,
1726,
1759,
1760,
2386,
2387,
2482,
2483,
2535,
2536,
2547,
2548,
3024,
3025,
3067,
3281,
3282,
3348,
3565,
3566,
3607,
3838,
3839,
3886,
4175,
4176,
4212,
4213,
4214,
4221,
4222,
4225,
4226,
4228,
4229,
4232,
4233,
4239,
4240,
4249,
4250,
4264,
4265
],
"line_end_idx": [
65,
66,
199,
200,
302,
303,
345,
346,
425,
426,
807,
808,
827,
828,
1229,
1230,
1258,
1259,
1725,
1726,
1759,
1760,
2386,
2387,
2482,
2483,
2535,
2536,
2547,
2548,
3024,
3025,
3067,
3281,
3282,
3348,
3565,
3566,
3607,
3838,
3839,
3886,
4175,
4176,
4212,
4213,
4214,
4221,
4222,
4225,
4226,
4228,
4229,
4232,
4233,
4239,
4240,
4249,
4250,
4264,
4265,
4335
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 4335,
"ccnet_original_nlines": 61,
"rps_doc_curly_bracket": 0,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.3436657786369324,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.10512129217386246,
"rps_doc_frac_unique_words": 0.43119266629219055,
"rps_doc_mean_word_length": 5.470948219299316,
"rps_doc_num_sentences": 41,
"rps_doc_symbol_to_word_ratio": 0,
"rps_doc_unigram_entropy": 5.081387996673584,
"rps_doc_word_count": 654,
"rps_doc_frac_chars_dupe_10grams": 0.05086641013622284,
"rps_doc_frac_chars_dupe_5grams": 0.10313024371862411,
"rps_doc_frac_chars_dupe_6grams": 0.07294578105211258,
"rps_doc_frac_chars_dupe_7grams": 0.05086641013622284,
"rps_doc_frac_chars_dupe_8grams": 0.05086641013622284,
"rps_doc_frac_chars_dupe_9grams": 0.05086641013622284,
"rps_doc_frac_chars_top_2gram": 0.00838456954807043,
"rps_doc_frac_chars_top_3gram": 0.01676913909614086,
"rps_doc_frac_chars_top_4gram": 0.01928452029824257,
"rps_doc_books_importance": -308.6877746582031,
"rps_doc_books_importance_length_correction": -308.6877746582031,
"rps_doc_openwebtext_importance": -212.5541229248047,
"rps_doc_openwebtext_importance_length_correction": -212.5541229248047,
"rps_doc_wikipedia_importance": -135.49964904785156,
"rps_doc_wikipedia_importance_length_correction": -135.49964904785156
},
"fasttext": {
"dclm": 0.9846441149711609,
"english": 0.9202039241790771,
"fineweb_edu_approx": 3.1988813877105713,
"eai_general_math": 0.00066995999077335,
"eai_open_web_math": 0.12634539604187012,
"eai_web_code": 0.00015270999574568123
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "612.82",
"labels": {
"level_1": "Industrial arts, Technology, and Engineering",
"level_2": "Medicine",
"level_3": "Physiology"
}
},
"secondary": {
"code": "613.7",
"labels": {
"level_1": "Industrial arts, Technology, and Engineering",
"level_2": "Medicine",
"level_3": "Health and Hygiene"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "2",
"label": "Understand"
},
"secondary": {
"code": "3",
"label": "Apply"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "2",
"label": "Conceptual"
},
"secondary": {
"code": "3",
"label": "Procedural"
}
},
"document_type_v1": {
"primary": {
"code": "3",
"label": "Reference/Encyclopedic/Educational"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "3",
"label": "Irrelevant Content"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"missing_content": {
"primary": {
"code": "4",
"label": "Missing Images or Figures"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"document_type_v2": {
"primary": {
"code": "10",
"label": "Knowledge Article"
},
"secondary": {
"code": "9",
"label": "FAQ"
}
},
"reasoning_depth": {
"primary": {
"code": "2",
"label": "Basic Reasoning"
},
"secondary": {
"code": "3",
"label": "Intermediate Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "3",
"label": "Mostly Correct"
},
"secondary": {
"code": "4",
"label": "Highly Correct"
}
},
"education_level": {
"primary": {
"code": "1",
"label": "General Audience"
},
"secondary": {
"code": "2",
"label": "High School Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
-3,735,092,766,298,594,300 |
enlightenment/src/modules/sysinfo/cpuclock/cpuclock.c
916 lines
27 KiB
C
#include "cpuclock.h"
#if defined(__OpenBSD__) || defined(__NetBSD__)
#include <sys/param.h>
#include <sys/sysctl.h>
#endif
typedef struct _Thread_Config Thread_Config;
struct _Thread_Config
{
int interval;
Instance *inst;
};
typedef struct _Pstate_Config Pstate_Config;
struct _Pstate_Config
{
Instance *inst;
int min;
int max;
int turbo;
};
static Cpu_Status *
_cpuclock_status_new(void)
{
Cpu_Status *s;
s = E_NEW(Cpu_Status, 1);
if (!s) return NULL;
s->active = -1;
return s;
}
static void
_cpuclock_status_free(Cpu_Status *s)
{
Eina_List *l;
if (s->frequencies) eina_list_free(s->frequencies);
if (s->governors)
{
for (l = s->governors; l; l = l->next)
E_FREE_FUNC(l->data, free);
eina_list_free(s->governors);
}
E_FREE_FUNC(s->cur_governor, free);
if (s->orig_governor) eina_stringshare_del(s->orig_governor);
E_FREE_FUNC(s, free);
}
static int
_cpuclock_cb_sort(const void *item1, const void *item2)
{
int a, b;
a = (long)item1;
b = (long)item2;
if (a < b) return -1;
else if (a > b)
return 1;
return 0;
}
static void
_cpuclock_set_thread_governor(void *data, Ecore_Thread *th EINA_UNUSED)
{
const char *governor = data;
_cpuclock_sysfs_setall("scaling_governor", governor);
if (!strcmp(governor, "ondemand"))
_cpuclock_sysfs_set("ondemand/ignore_nice_load", "0");
else if (!strcmp(governor, "conservative"))
_cpuclock_sysfs_set("conservative/ignore_nice_load", "0");
}
static void
_cpuclock_set_thread_frequency(void *data, Ecore_Thread *th EINA_UNUSED)
{
const char *freq = data;
#if defined __FreeBSD__ || defined __OpenBSD__
_cpuclock_sysctl_frequency(freq);
return;
#endif
_cpuclock_sysfs_setall("scaling_setspeed", freq);
}
static void
_cpuclock_set_thread_pstate(void *data, Ecore_Thread *th EINA_UNUSED)
{
Pstate_Config *pc = data;
_cpuclock_sysfs_pstate(pc->min, pc->max, pc->turbo);
}
static void
_cpuclock_set_thread_done(void *data EINA_UNUSED, Ecore_Thread *th EINA_UNUSED)
{
return;
}
static void
_cpuclock_set_thread_pstate_done(void *data, Ecore_Thread *th EINA_UNUSED)
{
Pstate_Config *pc = data;
E_FREE_FUNC(pc, free);
return;
}
void
_cpuclock_set_governor(const char *governor)
{
#if defined __FreeBSD__ || defined __OpenBSD__
return;
#endif
ecore_thread_run(_cpuclock_set_thread_governor, _cpuclock_set_thread_done, NULL, governor);
}
static void
_cpuclock_set_frequency(int frequency)
{
char buf[4096];
const char *freq;
#ifdef __FreeBSD__
frequency /= 1000;
#endif
snprintf(buf, sizeof(buf), "%i", frequency);
freq = eina_stringshare_add(buf);
ecore_thread_run(_cpuclock_set_thread_frequency, _cpuclock_set_thread_done, NULL, freq);
}
void
_cpuclock_set_pstate(int min, int max, int turbo)
{
#if defined __FreeBSD__ || defined __OpenBSD__
return;
#endif
Pstate_Config *pc;
pc = E_NEW(Pstate_Config, 1);
if (!pc) return;
pc->turbo = turbo;
pc->min = min;
pc->max = max;
ecore_thread_run(_cpuclock_set_thread_pstate, _cpuclock_set_thread_pstate_done, NULL, pc);
}
static void
_cpuclock_face_cb_set_frequency(void *data, Evas_Object *obj EINA_UNUSED, const char *emission, const char *src EINA_UNUSED)
{
Eina_List *l;
int next_frequency = 0;
Instance *inst = data;
for (l = inst->cfg->cpuclock.status->frequencies; l; l = l->next)
{
if (inst->cfg->cpuclock.status->cur_frequency == (long)l->data)
{
if (!strcmp(emission, "e,action,frequency,increase"))
{
if (l->next) next_frequency = (long)l->next->data;
break;
}
else if (!strcmp(emission, "e,action,frequency,decrease"))
{
if (l->prev) next_frequency = (long)l->prev->data;
break;
}
else
break;
}
}
if (inst->cfg->cpuclock.status->can_set_frequency && next_frequency)
_cpuclock_set_frequency(next_frequency);
}
static void
_cpuclock_face_cb_set_governor(void *data, Evas_Object *obj EINA_UNUSED, const char *emission EINA_UNUSED, const char *src EINA_UNUSED)
{
Eina_List *l;
char *next_governor = NULL;
Instance *inst = data;
for (l = inst->cfg->cpuclock.status->governors; l; l = l->next)
{
if (!strcmp(l->data, inst->cfg->cpuclock.status->cur_governor))
{
if (l->next)
next_governor = l->next->data;
else
next_governor = inst->cfg->cpuclock.status->governors->data;
break;
}
}
if (next_governor) _cpuclock_set_governor(next_governor);
}
static Eina_Bool
_cpuclock_event_cb_powersave(void *data, int type, void *event)
{
Instance *inst = data;
E_Event_Powersave_Update *ev;
Eina_List *l;
Eina_Bool has_powersave = EINA_FALSE;
Eina_Bool has_conservative = EINA_FALSE;
if (type != E_EVENT_POWERSAVE_UPDATE) return ECORE_CALLBACK_PASS_ON;
if (!inst->cfg->cpuclock.auto_powersave) return ECORE_CALLBACK_PASS_ON;
ev = event;
if (!inst->cfg->cpuclock.status->orig_governor)
inst->cfg->cpuclock.status->orig_governor = eina_stringshare_add(inst->cfg->cpuclock.status->cur_governor);
for (l = inst->cfg->cpuclock.status->governors; l; l = l->next)
{
if (!strcmp(l->data, "conservative"))
has_conservative = EINA_TRUE;
else if (!strcmp(l->data, "powersave"))
has_powersave = EINA_TRUE;
else if (!strcmp(l->data, "interactive"))
has_powersave = EINA_TRUE;
}
switch (ev->mode)
{
case E_POWERSAVE_MODE_NONE:
case E_POWERSAVE_MODE_LOW:
_cpuclock_set_governor(inst->cfg->cpuclock.status->orig_governor);
eina_stringshare_del(inst->cfg->cpuclock.status->orig_governor);
inst->cfg->cpuclock.status->orig_governor = NULL;
break;
case E_POWERSAVE_MODE_MEDIUM:
case E_POWERSAVE_MODE_HIGH:
if ((inst->cfg->cpuclock.powersave_governor) || (has_conservative))
{
if (inst->cfg->cpuclock.powersave_governor)
_cpuclock_set_governor(inst->cfg->cpuclock.powersave_governor);
else
_cpuclock_set_governor("conservative");
break;
}
case E_POWERSAVE_MODE_EXTREME:
if (has_powersave)
_cpuclock_set_governor("powersave");
break;
}
return ECORE_CALLBACK_PASS_ON;
}
void
_cpuclock_config_updated(Instance *inst)
{
Edje_Message_Int_Set *frequency_msg;
Edje_Message_String_Set *governor_msg;
Eina_List *l;
int i;
unsigned int count;
if (inst->cfg->cpuclock.status->frequencies)
{
count = eina_list_count(inst->cfg->cpuclock.status->frequencies);
frequency_msg = malloc(sizeof(Edje_Message_Int_Set) + (count - 1) * sizeof(int));
EINA_SAFETY_ON_NULL_RETURN(frequency_msg);
frequency_msg->count = count;
for (l = inst->cfg->cpuclock.status->frequencies, i = 0; l; l = l->next, i++)
frequency_msg->val[i] = (long)l->data;
edje_object_message_send(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), EDJE_MESSAGE_INT_SET, 1, frequency_msg);
free(frequency_msg);
}
if (inst->cfg->cpuclock.status->governors)
{
count = eina_list_count(inst->cfg->cpuclock.status->governors);
governor_msg = malloc(sizeof(Edje_Message_String_Set) + (count - 1) * sizeof(char *));
governor_msg->count = count;
for (l = inst->cfg->cpuclock.status->governors, i = 0; l; l = l->next, i++)
governor_msg->str[i] = (char *)l->data;
edje_object_message_send(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), EDJE_MESSAGE_STRING_SET, 2, governor_msg);
free(governor_msg);
}
}
static void
_cpuclock_face_update_current(Instance *inst)
{
Edje_Message_Int_Set *frequency_msg;
Edje_Message_String governor_msg;
frequency_msg = malloc(sizeof(Edje_Message_Int_Set) + (sizeof(int) * 4));
EINA_SAFETY_ON_NULL_RETURN(frequency_msg);
frequency_msg->count = 5;
frequency_msg->val[0] = inst->cfg->cpuclock.status->cur_frequency;
frequency_msg->val[1] = inst->cfg->cpuclock.status->can_set_frequency;
frequency_msg->val[2] = inst->cfg->cpuclock.status->cur_min_frequency;
frequency_msg->val[3] = inst->cfg->cpuclock.status->cur_max_frequency;
frequency_msg->val[4] = 0; // pad
edje_object_message_send(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), EDJE_MESSAGE_INT_SET, 3,
frequency_msg);
free(frequency_msg);
/* BSD crashes here without the if-condition
* since it has no governors (yet) */
if (inst->cfg->cpuclock.status->cur_governor)
{
governor_msg.str = inst->cfg->cpuclock.status->cur_governor;
edje_object_message_send(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), EDJE_MESSAGE_STRING, 4,
&governor_msg);
}
}
static void
_cpuclock_status_check_available(Cpu_Status *s)
{
char buf[4096];
Eina_List *l;
// FIXME: this assumes all cores accept the same freqs/ might be wrong
#if defined (__OpenBSD__)
int p;
if (s->frequencies)
{
eina_list_free(s->frequencies);
s->frequencies = NULL;
}
/* storing percents */
p = 100;
s->frequencies = eina_list_append(s->frequencies, (void *)p);
p = 75;
s->frequencies = eina_list_append(s->frequencies, (void *)p);
p = 50;
s->frequencies = eina_list_append(s->frequencies, (void *)p);
p = 25;
s->frequencies = eina_list_append(s->frequencies, (void *)p);
#elif defined (__FreeBSD__)
int freq;
size_t len = sizeof(buf);
char *pos, *q;
/* read freq_levels sysctl and store it in freq */
if (sysctlbyname("dev.cpu.0.freq_levels", buf, &len, NULL, 0) == 0)
{
/* sysctl returns 0 on success */
if (s->frequencies)
{
eina_list_free(s->frequencies);
s->frequencies = NULL;
}
/* parse freqs and store the frequencies in s->frequencies */
pos = buf;
while (pos)
{
q = strchr(pos, '/');
if (!q) break;
*q = '\0';
freq = atoi(pos);
freq *= 1000;
s->frequencies = eina_list_append(s->frequencies, (void *)freq);
pos = q + 1;
pos = strchr(pos, ' ');
}
}
/* sort is not necessary because freq_levels is already sorted */
/* freebsd doesn't have governors */
if (s->governors)
{
for (l = s->governors; l; l = l->next)
free(l->data);
eina_list_free(s->governors);
s->governors = NULL;
}
#else
FILE *f;
f = fopen("/sys/devices/system/cpu/cpu0/cpufreq/scaling_available_frequencies", "r");
if (f)
{
char *freq;
if (s->frequencies)
{
eina_list_free(s->frequencies);
s->frequencies = NULL;
}
if (fgets(buf, sizeof(buf), f) == NULL)
{
fclose(f);
return;
}
fclose(f);
freq = strtok(buf, " ");
do
{
if (atoi(freq) != 0)
{
s->frequencies = eina_list_append(s->frequencies,
(void *)(long)atoi(freq));
}
freq = strtok(NULL, " ");
}
while (freq);
s->frequencies = eina_list_sort(s->frequencies,
eina_list_count(s->frequencies),
_cpuclock_cb_sort);
}
else
do
{
#define CPUFREQ_SYSFSDIR "/sys/devices/system/cpu/cpu0/cpufreq"
f = fopen(CPUFREQ_SYSFSDIR "/scaling_cur_freq", "r");
if (!f) break;
fclose(f);
f = fopen(CPUFREQ_SYSFSDIR "/scaling_driver", "r");
if (!f) break;
if (fgets(buf, sizeof(buf), f) == NULL)
{
fclose(f);
break;
}
fclose(f);
if (strcmp(buf, "intel_pstate\n")) break;
if (s->frequencies)
{
eina_list_free(s->frequencies);
s->frequencies = NULL;
}
#define CPUFREQ_ADDF(filename) \
f = fopen(CPUFREQ_SYSFSDIR filename, "r"); \
if (f) \
{ \
if (fgets(buf, sizeof(buf), f) != NULL) \
s->frequencies = eina_list_append(s->frequencies, \
(void *)(long)(atoi(buf))); \
fclose(f); \
}
CPUFREQ_ADDF("/cpuinfo_min_freq");
CPUFREQ_ADDF("/cpuinfo_max_freq");
}
while (0);
f = fopen("/sys/devices/system/cpu/cpu0/cpufreq/scaling_available_governors", "r");
if (f)
{
char *gov;
int len;
if (s->governors)
{
for (l = s->governors; l; l = l->next)
free(l->data);
eina_list_free(s->governors);
s->governors = NULL;
}
if (fgets(buf, sizeof(buf), f) == NULL)
{
fclose(f);
return;
}
fclose(f);
len = strlen(buf);
if (len > 0)
{
gov = buf + len - 1;
while ((gov > buf) && (isspace(*gov)))
{
*gov = 0;
gov--;
}
}
gov = strtok(buf, " ");
do
{
while ((*gov) && (isspace(*gov)))
gov++;
if (strlen(gov) != 0)
s->governors = eina_list_append(s->governors, strdup(gov));
gov = strtok(NULL, " ");
}
while (gov);
s->governors =
eina_list_sort(s->governors, eina_list_count(s->governors),
(int (*)(const void *, const void *))strcmp);
}
#endif
}
static int
_cpuclock_status_check_current(Cpu_Status *s)
{
int ret = 0;
int frequency = 0;
#if defined (__OpenBSD__)
size_t len = sizeof(frequency);
int percent, mib[] = {CTL_HW, HW_CPUSPEED};
s->active = 0;
_cpuclock_status_check_available(s);
if (sysctl(mib, 2, &frequency, &len, NULL, 0) == 0)
{
frequency *= 1000;
if (frequency != s->cur_frequency) ret = 1;
s->cur_frequency = frequency;
s->active = 1;
}
mib[1] = HW_SETPERF;
if (sysctl(mib, 2, &percent, &len, NULL, 0) == 0)
{
s->cur_percent = percent;
}
s->can_set_frequency = 1;
s->cur_governor = NULL;
#elif defined (__FreeBSD__)
size_t len = sizeof(frequency);
s->active = 0;
/* frequency is stored in dev.cpu.0.freq */
if (sysctlbyname("dev.cpu.0.freq", &frequency, &len, NULL, 0) == 0)
{
frequency *= 1000;
if (frequency != s->cur_frequency) ret = 1;
s->cur_frequency = frequency;
s->active = 1;
}
/* hardcoded for testing */
s->can_set_frequency = 1;
s->cur_governor = NULL;
#else
char buf[4096];
FILE *f;
int frequency_min = 0x7fffffff;
int frequency_max = 0;
int freqtot = 0;
int i;
s->active = 0;
_cpuclock_status_check_available(s);
// average out frequencies of all cores
for (i = 0; i < 64; i++)
{
snprintf(buf, sizeof(buf), "/sys/devices/system/cpu/cpu%i/cpufreq/scaling_cur_freq", i);
f = fopen(buf, "r");
if (f)
{
if (fgets(buf, sizeof(buf), f) == NULL)
{
fclose(f);
continue;
}
fclose(f);
frequency = atoi(buf);
if (frequency > frequency_max) frequency_max = frequency;
if (frequency < frequency_min) frequency_min = frequency;
freqtot += frequency;
s->active = 1;
}
else
break;
}
if (i < 1) i = 1;
frequency = freqtot / i;
if (frequency != s->cur_frequency) ret = 1;
if (frequency_min != s->cur_min_frequency) ret = 1;
if (frequency_max != s->cur_max_frequency) ret = 1;
s->cur_frequency = frequency;
s->cur_min_frequency = frequency_min;
s->cur_max_frequency = frequency_max;
// printf("%i | %i %i\n", frequency, frequency_min, frequency_max);
// FIXME: this assumes all cores are on the same governor
f = fopen("/sys/devices/system/cpu/cpu0/cpufreq/scaling_setspeed", "r");
if (f)
{
s->can_set_frequency = 1;
fclose(f);
}
else
{
s->can_set_frequency = 0;
}
f = fopen("/sys/devices/system/cpu/cpu0/cpufreq/scaling_governor", "r");
if (f)
{
char *p;
if (fgets(buf, sizeof(buf), f) == NULL)
{
fclose(f);
return ret;
}
fclose(f);
for (p = buf; (*p != 0) && (isalnum(*p)); p++) ;
*p = 0;
if ((!s->cur_governor) || (strcmp(buf, s->cur_governor)))
{
ret = 1;
free(s->cur_governor);
s->cur_governor = strdup(buf);
for (i = strlen(s->cur_governor) - 1; i >= 0; i--)
{
if (isspace(s->cur_governor[i]))
s->cur_governor[i] = 0;
else
break;
}
}
}
f = fopen("/sys/devices/system/cpu/intel_pstate/min_perf_pct", "r");
if (f)
{
if (fgets(buf, sizeof(buf), f) != NULL)
{
s->pstate_min = atoi(buf);
s->pstate = 1;
}
fclose(f);
}
f = fopen("/sys/devices/system/cpu/intel_pstate/max_perf_pct", "r");
if (f)
{
if (fgets(buf, sizeof(buf), f) != NULL)
{
s->pstate_max = atoi(buf);
s->pstate = 1;
}
fclose(f);
}
f = fopen("/sys/devices/system/cpu/intel_pstate/no_turbo", "r");
if (f)
{
if (fgets(buf, sizeof(buf), f) != NULL)
{
s->pstate_turbo = atoi(buf);
if (s->pstate_turbo) s->pstate_turbo = 0;
else s->pstate_turbo = 1;
s->pstate = 1;
}
fclose(f);
}
#endif
return ret;
}
static void
_cpuclock_cb_frequency_check_main(void *data, Ecore_Thread *th)
{
Thread_Config *thc = data;
for (;;)
{
Cpu_Status *status;
if (ecore_thread_check(th)) break;
status = _cpuclock_status_new();
if (_cpuclock_status_check_current(status))
ecore_thread_feedback(th, status);
else
_cpuclock_status_free(status);
if (ecore_thread_check(th)) break;
usleep((1000000.0 / 8.0) * (double)thc->interval);
}
E_FREE_FUNC(thc, free);
}
static void
_cpuclock_cb_frequency_check_notify(void *data,
Ecore_Thread *th EINA_UNUSED,
void *msg)
{
Cpu_Status *status = msg;
Eina_Bool freq_changed = EINA_FALSE;
Thread_Config *thc = data;
Instance *inst = thc->inst;
if (inst->cfg->esm != E_SYSINFO_MODULE_CPUCLOCK && inst->cfg->esm != E_SYSINFO_MODULE_SYSINFO) return;
if (!inst->cfg) return;
if ((inst->cfg->cpuclock.status) && (status) &&
(
#ifdef __OpenBSD__
(status->cur_percent != inst->cfg->cpuclock.status->cur_percent ) ||
#endif
(status->cur_frequency != inst->cfg->cpuclock.status->cur_frequency ) ||
(status->cur_min_frequency != inst->cfg->cpuclock.status->cur_min_frequency) ||
(status->cur_max_frequency != inst->cfg->cpuclock.status->cur_max_frequency) ||
(status->can_set_frequency != inst->cfg->cpuclock.status->can_set_frequency)))
freq_changed = EINA_TRUE;
_cpuclock_status_free(inst->cfg->cpuclock.status);
inst->cfg->cpuclock.status = status;
if (freq_changed)
{
_cpuclock_face_update_current(inst);
}
if (inst->cfg->cpuclock.status->active == 0)
elm_layout_signal_emit(inst->cfg->cpuclock.o_gadget, "e,state,disabled", "e");
else if (inst->cfg->cpuclock.status->active == 1)
elm_layout_signal_emit(inst->cfg->cpuclock.o_gadget, "e,state,enabled", "e");
_cpuclock_set_pstate(inst->cfg->cpuclock.pstate_min - 1,
inst->cfg->cpuclock.pstate_max - 1, inst->cfg->cpuclock.status->pstate_turbo);
}
void
_cpuclock_poll_interval_update(Instance *inst)
{
Thread_Config *thc;
if (inst->cfg->cpuclock.frequency_check_thread)
{
ecore_thread_cancel(inst->cfg->cpuclock.frequency_check_thread);
inst->cfg->cpuclock.frequency_check_thread = NULL;
}
thc = E_NEW(Thread_Config, 1);
if (thc)
{
thc->inst = inst;
thc->interval = inst->cfg->cpuclock.poll_interval;
inst->cfg->cpuclock.frequency_check_thread =
ecore_thread_feedback_run(_cpuclock_cb_frequency_check_main,
_cpuclock_cb_frequency_check_notify,
NULL, NULL, thc, EINA_TRUE);
}
e_config_save_queue();
}
static void
_cpuclock_removed_cb(void *data, Evas_Object *obj EINA_UNUSED, void *event_data)
{
Instance *inst = data;
if (inst->o_main != event_data) return;
if (inst->cfg->cpuclock.handler)
ecore_event_handler_del(inst->cfg->cpuclock.handler);
if (inst->cfg->cpuclock.frequency_check_thread)
{
ecore_thread_cancel(inst->cfg->cpuclock.frequency_check_thread);
inst->cfg->cpuclock.frequency_check_thread = NULL;
}
if (inst->cfg->cpuclock.governor)
eina_stringshare_del(inst->cfg->cpuclock.governor);
if (inst->cfg->cpuclock.status) _cpuclock_status_free(inst->cfg->cpuclock.status);
sysinfo_config->items = eina_list_remove(sysinfo_config->items, inst->cfg);
E_FREE(inst->cfg);
}
void
sysinfo_cpuclock_remove(Instance *inst)
{
if (inst->cfg->cpuclock.handler)
ecore_event_handler_del(inst->cfg->cpuclock.handler);
if (inst->cfg->cpuclock.frequency_check_thread)
{
ecore_thread_cancel(inst->cfg->cpuclock.frequency_check_thread);
inst->cfg->cpuclock.frequency_check_thread = NULL;
}
if (inst->cfg->cpuclock.governor)
eina_stringshare_del(inst->cfg->cpuclock.governor);
if (inst->cfg->cpuclock.status) _cpuclock_status_free(inst->cfg->cpuclock.status);
}
static void
_cpuclock_created_cb(void *data, Evas_Object *obj, void *event_data EINA_UNUSED)
{
Instance *inst = data;
if (inst->cfg->cpuclock.pstate_min == 0) inst->cfg->cpuclock.pstate_min = 1;
if (inst->cfg->cpuclock.pstate_max == 0) inst->cfg->cpuclock.pstate_max = 101;
inst->cfg->cpuclock.o_gadget = elm_layout_add(inst->o_main);
e_theme_edje_object_set(inst->cfg->cpuclock.o_gadget, "base/theme/modules/cpufreq",
"e/modules/cpufreq/main");
E_EXPAND(inst->cfg->cpuclock.o_gadget);
E_FILL(inst->cfg->cpuclock.o_gadget);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,governor,next", "*",
_cpuclock_face_cb_set_governor, inst);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,frequency,increase", "*",
_cpuclock_face_cb_set_frequency, inst);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,frequency,decrease", "*",
_cpuclock_face_cb_set_frequency, inst);
elm_box_pack_end(inst->o_main, inst->cfg->cpuclock.o_gadget);
evas_object_show(inst->cfg->cpuclock.o_gadget);
evas_object_smart_callback_del_full(obj, "gadget_created", _cpuclock_created_cb, data);
inst->cfg->cpuclock.status = _cpuclock_status_new();
_cpuclock_status_check_available(inst->cfg->cpuclock.status);
_cpuclock_poll_interval_update(inst);
inst->cfg->cpuclock.handler = ecore_event_handler_add(E_EVENT_POWERSAVE_UPDATE,
_cpuclock_event_cb_powersave, inst);
_cpuclock_config_updated(inst);
}
Evas_Object *
sysinfo_cpuclock_create(Evas_Object *parent, Instance *inst)
{
if (inst->cfg->cpuclock.pstate_min == 0) inst->cfg->cpuclock.pstate_min = 1;
if (inst->cfg->cpuclock.pstate_max == 0) inst->cfg->cpuclock.pstate_max = 101;
inst->cfg->cpuclock.o_gadget = elm_layout_add(parent);
e_theme_edje_object_set(inst->cfg->cpuclock.o_gadget, "base/theme/modules/cpufreq",
"e/modules/cpufreq/main");
E_EXPAND(inst->cfg->cpuclock.o_gadget);
E_FILL(inst->cfg->cpuclock.o_gadget);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,governor,next", "*",
_cpuclock_face_cb_set_governor, inst);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,frequency,increase", "*",
_cpuclock_face_cb_set_frequency, inst);
edje_object_signal_callback_add(elm_layout_edje_get(inst->cfg->cpuclock.o_gadget), "e,action,frequency,decrease", "*",
_cpuclock_face_cb_set_frequency, inst);
evas_object_show(inst->cfg->cpuclock.o_gadget);
inst->cfg->cpuclock.status = _cpuclock_status_new();
_cpuclock_status_check_available(inst->cfg->cpuclock.status);
_cpuclock_poll_interval_update(inst);
inst->cfg->cpuclock.handler = ecore_event_handler_add(E_EVENT_POWERSAVE_UPDATE,
_cpuclock_event_cb_powersave, inst);
_cpuclock_config_updated(inst);
return inst->cfg->cpuclock.o_gadget;
}
static Config_Item *
_conf_item_get(int *id)
{
Config_Item *ci;
Eina_List *l;
if (*id > 0)
{
EINA_LIST_FOREACH(sysinfo_config->items, l, ci)
if (*id == ci->id && ci->esm == E_SYSINFO_MODULE_CPUCLOCK) return ci;
}
ci = E_NEW(Config_Item, 1);
if (*id != -1)
ci->id = eina_list_count(sysinfo_config->items)+1;
else
ci->id = -1;
ci->esm = E_SYSINFO_MODULE_CPUCLOCK;
ci->cpuclock.poll_interval = 32;
ci->cpuclock.restore_governor = 0;
ci->cpuclock.auto_powersave = 1;
ci->cpuclock.powersave_governor = NULL;
ci->cpuclock.governor = NULL;
ci->cpuclock.pstate_min = 1;
ci->cpuclock.pstate_max = 101;
sysinfo_config->items = eina_list_append(sysinfo_config->items, ci);
return ci;
}
Evas_Object *
cpuclock_create(Evas_Object *parent, int *id, E_Gadget_Site_Orient orient EINA_UNUSED)
{
Instance *inst;
inst = E_NEW(Instance, 1);
inst->cfg = _conf_item_get(id);
*id = inst->cfg->id;
inst->o_main = elm_box_add(parent);
E_EXPAND(inst->o_main);
evas_object_size_hint_aspect_set(inst->o_main, EVAS_ASPECT_CONTROL_BOTH, 1, 1);
evas_object_smart_callback_add(parent, "gadget_created", _cpuclock_created_cb, inst);
evas_object_smart_callback_add(parent, "gadget_removed", _cpuclock_removed_cb, inst);
evas_object_show(inst->o_main);
if (inst->cfg->id < 0) return inst->o_main;
sysinfo_instances =
eina_list_append(sysinfo_instances, inst);
return inst->o_main;
}
|
{
"url": "https://git.enlightenment.org/enlightenment/enlightenment/src/commit/4c2116ac0ffcda1e06a3de0767bd00087ccaa6f2/src/modules/sysinfo/cpuclock/cpuclock.c",
"source_domain": "git.enlightenment.org",
"snapshot_id": "CC-MAIN-2024-10",
"warc_metadata": {
"Content-Length": "393965",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:CUCRULAE42MASWYKKMZFRJ3BAJP73K6M",
"WARC-Concurrent-To": "<urn:uuid:75cda72d-dc24-4206-8418-863032ce258e>",
"WARC-Date": "2024-03-01T01:31:02Z",
"WARC-IP-Address": "140.211.167.131",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:VIJJ2KEYNNIRXZYHMBI2SR7JSGP6HVX2",
"WARC-Record-ID": "<urn:uuid:167b763d-7f52-42e5-95e9-85c087b4341c>",
"WARC-Target-URI": "https://git.enlightenment.org/enlightenment/enlightenment/src/commit/4c2116ac0ffcda1e06a3de0767bd00087ccaa6f2/src/modules/sysinfo/cpuclock/cpuclock.c",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:247cca9d-6162-44af-ae8d-bdc8b0d0054f>"
},
"warc_info": "isPartOf: CC-MAIN-2024-10\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for February/March 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-183\r\nsoftware: Apache Nutch 1.19 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
54,
55,
65,
72,
74,
75,
97,
145,
168,
192,
199,
244,
266,
268,
282,
298,
301,
346,
368,
370,
386,
395,
404,
415,
418,
438,
465,
467,
482,
508,
529,
545,
555,
557,
569,
606,
608,
622,
674,
692,
694,
733,
761,
791,
793,
829,
891,
913,
915,
926,
982,
984,
994,
1011,
1028,
1050,
1066,
1076,
1086,
1088,
1100,
1172,
1174,
1203,
1257,
1292,
1347,
1391,
1450,
1452,
1464,
1537,
1539,
1564,
1611,
1645,
1653,
1660,
1710,
1712,
1724,
1794,
1796,
1822,
1875,
1877,
1889,
1969,
1971,
1979,
1981,
1993,
2068,
2070,
2096,
2119,
2127,
2129,
2134,
2179,
2181,
2228,
2236,
2243,
2335,
2337,
2349,
2388,
2390,
2406,
2424,
2443,
2462,
2469,
2514,
2548,
2637,
2639,
2644,
2694,
2696,
2743,
2751,
2758,
2777,
2807,
2824,
2843,
2858,
2873,
2964,
2966,
2978,
3103,
3105,
3119,
3143,
3166,
3232,
3234,
3298,
3300,
3354,
3356,
3407,
3414,
3416,
3475,
3477,
3528,
3535,
3537,
3542,
3549,
3551,
3553,
3622,
3663,
3665,
3677,
3813,
3815,
3829,
3857,
3880,
3944,
3946,
4010,
4012,
4025,
4056,
4061,
4122,
4129,
4131,
4133,
4191,
4193,
4210,
4274,
4276,
4299,
4329,
4343,
4381,
4422,
4491,
4563,
4575,
4623,
4731,
4795,
4797,
4835,
4865,
4905,
4932,
4974,
5001,
5003,
5021,
5023,
5051,
5078,
5145,
5210,
5260,
5267,
5297,
5325,
5393,
5395,
5439,
5503,
5508,
5548,
5555,
5557,
5588,
5607,
5644,
5651,
5653,
5684,
5686,
5691,
5732,
5734,
5771,
5810,
5824,
5831,
5851,
5896,
5898,
5964,
6046,
6089,
6119,
6197,
6236,
6353,
6374,
6376,
6419,
6421,
6485,
6572,
6601,
6677,
6717,
6836,
6856,
6858,
6860,
6872,
6918,
6920,
6957,
6991,
7065,
7108,
7134,
7201,
7272,
7343,
7414,
7448,
7549,
7565,
7586,
7631,
7668,
7714,
7716,
7777,
7877,
7893,
7895,
7897,
7909,
7957,
7959,
7975,
7989,
8060,
8086,
8093,
8113,
8115,
8147,
8170,
8172,
8195,
8204,
8266,
8274,
8336,
8344,
8406,
8414,
8476,
8504,
8514,
8540,
8555,
8606,
8674,
8676,
8710,
8730,
8732,
8764,
8787,
8789,
8851,
8862,
8874,
8876,
8898,
8913,
8924,
8942,
8956,
9021,
9034,
9058,
9060,
9062,
9128,
9165,
9183,
9185,
9224,
9239,
9269,
9290,
9292,
9298,
9307,
9393,
9400,
9402,
9414,
9434,
9436,
9468,
9491,
9493,
9533,
9535,
9546,
9554,
9556,
9567,
9592,
9595,
9597,
9618,
9620,
9670,
9697,
9699,
9725,
9727,
9741,
9789,
9822,
9842,
9844,
9849,
9852,
9854,
9918,
9972,
9987,
9998,
10050,
10065,
10105,
10107,
10118,
10125,
10127,
10138,
10180,
10200,
10202,
10234,
10257,
10259,
10292,
10337,
10346,
10350,
10392,
10444,
10474,
10487,
10489,
10524,
10559,
10561,
10572,
10656,
10663,
10665,
10676,
10685,
10703,
10705,
10744,
10759,
10789,
10810,
10812,
10852,
10854,
10865,
10873,
10875,
10886,
10905,
10918,
10920,
10941,
10980,
10982,
10992,
10999,
11001,
11003,
11027,
11030,
11032,
11066,
11073,
11095,
11155,
11180,
11182,
11195,
11210,
11270,
11316,
11318,
11325,
11327,
11338,
11384,
11386,
11399,
11418,
11444,
11476,
11520,
11535,
11572,
11624,
11626,
11645,
11689,
11719,
11734,
11736,
11757,
11807,
11809,
11835,
11837,
11863,
11887,
11915,
11947,
11962,
12006,
12074,
12076,
12095,
12139,
12169,
12184,
12186,
12214,
12240,
12264,
12270,
12286,
12295,
12327,
12350,
12367,
12374,
12389,
12426,
12466,
12491,
12493,
12582,
12603,
12610,
12612,
12652,
12654,
12665,
12675,
12677,
12688,
12711,
12769,
12827,
12849,
12864,
12866,
12871,
12878,
12880,
12898,
12923,
12967,
13019,
13071,
13101,
13139,
13177,
13245,
13303,
13376,
13383,
13385,
13411,
13422,
13424,
13429,
13431,
13457,
13459,
13532,
13539,
13541,
13550,
13590,
13592,
13603,
13615,
13617,
13628,
13677,
13685,
13743,
13745,
13754,
13777,
13808,
13859,
13861,
13894,
13918,
13923,
13930,
13932,
13934,
13936,
14005,
14012,
14014,
14054,
14056,
14083,
14098,
14100,
14111,
14113,
14182,
14189,
14191,
14231,
14233,
14260,
14275,
14277,
14288,
14290,
14355,
14362,
14364,
14404,
14406,
14435,
14477,
14503,
14518,
14520,
14531,
14533,
14540,
14552,
14554,
14566,
14630,
14632,
14659,
14668,
14670,
14690,
14725,
14758,
14802,
14837,
14842,
14873,
14908,
14959,
14961,
14985,
14987,
14999,
15047,
15077,
15088,
15090,
15116,
15153,
15180,
15208,
15311,
15335,
15383,
15385,
15404,
15473,
15480,
15553,
15633,
15713,
15792,
15818,
15869,
15906,
15924,
15926,
15963,
15965,
16010,
16089,
16139,
16217,
16274,
16353,
16355,
16360,
16407,
16409,
16429,
16477,
16479,
16544,
16595,
16597,
16628,
16637,
16639,
16657,
16708,
16753,
16814,
16851,
16880,
16882,
16905,
16907,
16919,
17000,
17002,
17025,
17065,
17098,
17152,
17200,
17202,
17267,
17318,
17320,
17354,
17406,
17489,
17565,
17584,
17586,
17591,
17631,
17633,
17666,
17720,
17768,
17770,
17835,
17886,
17888,
17922,
17974,
18057,
18059,
18071,
18152,
18154,
18177,
18254,
18333,
18394,
18478,
18505,
18545,
18583,
18697,
18736,
18855,
18895,
19014,
19054,
19116,
19164,
19252,
19305,
19367,
19405,
19485,
19522,
19554,
19556,
19570,
19631,
19633,
19710,
19789,
19844,
19928,
19955,
19995,
20033,
20147,
20186,
20305,
20345,
20464,
20504,
20552,
20605,
20667,
20705,
20785,
20822,
20854,
20891,
20893,
20914,
20938,
20940,
20957,
20971,
20984,
20986,
21034,
21104,
21106,
21134,
21149,
21200,
21205,
21218,
21255,
21288,
21323,
21356,
21396,
21426,
21455,
21486,
21555,
21566,
21568,
21582,
21669,
21671,
21687,
21714,
21746,
21767,
21803,
21827,
21907,
21993,
22079,
22111,
22155,
22175,
22218,
22239
],
"line_end_idx": [
54,
55,
65,
72,
74,
75,
97,
145,
168,
192,
199,
244,
266,
268,
282,
298,
301,
346,
368,
370,
386,
395,
404,
415,
418,
438,
465,
467,
482,
508,
529,
545,
555,
557,
569,
606,
608,
622,
674,
692,
694,
733,
761,
791,
793,
829,
891,
913,
915,
926,
982,
984,
994,
1011,
1028,
1050,
1066,
1076,
1086,
1088,
1100,
1172,
1174,
1203,
1257,
1292,
1347,
1391,
1450,
1452,
1464,
1537,
1539,
1564,
1611,
1645,
1653,
1660,
1710,
1712,
1724,
1794,
1796,
1822,
1875,
1877,
1889,
1969,
1971,
1979,
1981,
1993,
2068,
2070,
2096,
2119,
2127,
2129,
2134,
2179,
2181,
2228,
2236,
2243,
2335,
2337,
2349,
2388,
2390,
2406,
2424,
2443,
2462,
2469,
2514,
2548,
2637,
2639,
2644,
2694,
2696,
2743,
2751,
2758,
2777,
2807,
2824,
2843,
2858,
2873,
2964,
2966,
2978,
3103,
3105,
3119,
3143,
3166,
3232,
3234,
3298,
3300,
3354,
3356,
3407,
3414,
3416,
3475,
3477,
3528,
3535,
3537,
3542,
3549,
3551,
3553,
3622,
3663,
3665,
3677,
3813,
3815,
3829,
3857,
3880,
3944,
3946,
4010,
4012,
4025,
4056,
4061,
4122,
4129,
4131,
4133,
4191,
4193,
4210,
4274,
4276,
4299,
4329,
4343,
4381,
4422,
4491,
4563,
4575,
4623,
4731,
4795,
4797,
4835,
4865,
4905,
4932,
4974,
5001,
5003,
5021,
5023,
5051,
5078,
5145,
5210,
5260,
5267,
5297,
5325,
5393,
5395,
5439,
5503,
5508,
5548,
5555,
5557,
5588,
5607,
5644,
5651,
5653,
5684,
5686,
5691,
5732,
5734,
5771,
5810,
5824,
5831,
5851,
5896,
5898,
5964,
6046,
6089,
6119,
6197,
6236,
6353,
6374,
6376,
6419,
6421,
6485,
6572,
6601,
6677,
6717,
6836,
6856,
6858,
6860,
6872,
6918,
6920,
6957,
6991,
7065,
7108,
7134,
7201,
7272,
7343,
7414,
7448,
7549,
7565,
7586,
7631,
7668,
7714,
7716,
7777,
7877,
7893,
7895,
7897,
7909,
7957,
7959,
7975,
7989,
8060,
8086,
8093,
8113,
8115,
8147,
8170,
8172,
8195,
8204,
8266,
8274,
8336,
8344,
8406,
8414,
8476,
8504,
8514,
8540,
8555,
8606,
8674,
8676,
8710,
8730,
8732,
8764,
8787,
8789,
8851,
8862,
8874,
8876,
8898,
8913,
8924,
8942,
8956,
9021,
9034,
9058,
9060,
9062,
9128,
9165,
9183,
9185,
9224,
9239,
9269,
9290,
9292,
9298,
9307,
9393,
9400,
9402,
9414,
9434,
9436,
9468,
9491,
9493,
9533,
9535,
9546,
9554,
9556,
9567,
9592,
9595,
9597,
9618,
9620,
9670,
9697,
9699,
9725,
9727,
9741,
9789,
9822,
9842,
9844,
9849,
9852,
9854,
9918,
9972,
9987,
9998,
10050,
10065,
10105,
10107,
10118,
10125,
10127,
10138,
10180,
10200,
10202,
10234,
10257,
10259,
10292,
10337,
10346,
10350,
10392,
10444,
10474,
10487,
10489,
10524,
10559,
10561,
10572,
10656,
10663,
10665,
10676,
10685,
10703,
10705,
10744,
10759,
10789,
10810,
10812,
10852,
10854,
10865,
10873,
10875,
10886,
10905,
10918,
10920,
10941,
10980,
10982,
10992,
10999,
11001,
11003,
11027,
11030,
11032,
11066,
11073,
11095,
11155,
11180,
11182,
11195,
11210,
11270,
11316,
11318,
11325,
11327,
11338,
11384,
11386,
11399,
11418,
11444,
11476,
11520,
11535,
11572,
11624,
11626,
11645,
11689,
11719,
11734,
11736,
11757,
11807,
11809,
11835,
11837,
11863,
11887,
11915,
11947,
11962,
12006,
12074,
12076,
12095,
12139,
12169,
12184,
12186,
12214,
12240,
12264,
12270,
12286,
12295,
12327,
12350,
12367,
12374,
12389,
12426,
12466,
12491,
12493,
12582,
12603,
12610,
12612,
12652,
12654,
12665,
12675,
12677,
12688,
12711,
12769,
12827,
12849,
12864,
12866,
12871,
12878,
12880,
12898,
12923,
12967,
13019,
13071,
13101,
13139,
13177,
13245,
13303,
13376,
13383,
13385,
13411,
13422,
13424,
13429,
13431,
13457,
13459,
13532,
13539,
13541,
13550,
13590,
13592,
13603,
13615,
13617,
13628,
13677,
13685,
13743,
13745,
13754,
13777,
13808,
13859,
13861,
13894,
13918,
13923,
13930,
13932,
13934,
13936,
14005,
14012,
14014,
14054,
14056,
14083,
14098,
14100,
14111,
14113,
14182,
14189,
14191,
14231,
14233,
14260,
14275,
14277,
14288,
14290,
14355,
14362,
14364,
14404,
14406,
14435,
14477,
14503,
14518,
14520,
14531,
14533,
14540,
14552,
14554,
14566,
14630,
14632,
14659,
14668,
14670,
14690,
14725,
14758,
14802,
14837,
14842,
14873,
14908,
14959,
14961,
14985,
14987,
14999,
15047,
15077,
15088,
15090,
15116,
15153,
15180,
15208,
15311,
15335,
15383,
15385,
15404,
15473,
15480,
15553,
15633,
15713,
15792,
15818,
15869,
15906,
15924,
15926,
15963,
15965,
16010,
16089,
16139,
16217,
16274,
16353,
16355,
16360,
16407,
16409,
16429,
16477,
16479,
16544,
16595,
16597,
16628,
16637,
16639,
16657,
16708,
16753,
16814,
16851,
16880,
16882,
16905,
16907,
16919,
17000,
17002,
17025,
17065,
17098,
17152,
17200,
17202,
17267,
17318,
17320,
17354,
17406,
17489,
17565,
17584,
17586,
17591,
17631,
17633,
17666,
17720,
17768,
17770,
17835,
17886,
17888,
17922,
17974,
18057,
18059,
18071,
18152,
18154,
18177,
18254,
18333,
18394,
18478,
18505,
18545,
18583,
18697,
18736,
18855,
18895,
19014,
19054,
19116,
19164,
19252,
19305,
19367,
19405,
19485,
19522,
19554,
19556,
19570,
19631,
19633,
19710,
19789,
19844,
19928,
19955,
19995,
20033,
20147,
20186,
20305,
20345,
20464,
20504,
20552,
20605,
20667,
20705,
20785,
20822,
20854,
20891,
20893,
20914,
20938,
20940,
20957,
20971,
20984,
20986,
21034,
21104,
21106,
21134,
21149,
21200,
21205,
21218,
21255,
21288,
21323,
21356,
21396,
21426,
21455,
21486,
21555,
21566,
21568,
21582,
21669,
21671,
21687,
21714,
21746,
21767,
21803,
21827,
21907,
21993,
22079,
22111,
22155,
22175,
22218,
22239,
22240
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 22240,
"ccnet_original_nlines": 798,
"rps_doc_curly_bracket": 0.00791366957128048,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.09745272248983383,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.021999230608344078,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.5351215600967407,
"rps_doc_frac_unique_words": 0.29378530383110046,
"rps_doc_mean_word_length": 9.737602233886719,
"rps_doc_num_sentences": 166,
"rps_doc_symbol_to_word_ratio": 0.004824390169233084,
"rps_doc_unigram_entropy": 5.33601188659668,
"rps_doc_word_count": 1593,
"rps_doc_frac_chars_dupe_10grams": 0.14272820949554443,
"rps_doc_frac_chars_dupe_5grams": 0.2744971513748169,
"rps_doc_frac_chars_dupe_6grams": 0.22833934426307678,
"rps_doc_frac_chars_dupe_7grams": 0.21596184372901917,
"rps_doc_frac_chars_dupe_8grams": 0.1968153715133667,
"rps_doc_frac_chars_dupe_9grams": 0.17870035767555237,
"rps_doc_frac_chars_top_2gram": 0.00966992974281311,
"rps_doc_frac_chars_top_3gram": 0.0028365100733935833,
"rps_doc_frac_chars_top_4gram": 0.01160391978919506,
"rps_doc_books_importance": -2244.9521484375,
"rps_doc_books_importance_length_correction": -2244.9521484375,
"rps_doc_openwebtext_importance": -1329.8023681640625,
"rps_doc_openwebtext_importance_length_correction": -1329.8023681640625,
"rps_doc_wikipedia_importance": -810.4707641601562,
"rps_doc_wikipedia_importance_length_correction": -810.4707641601562
},
"fasttext": {
"dclm": 0.999048113822937,
"english": 0.3493238389492035,
"fineweb_edu_approx": 3.5732576847076416,
"eai_general_math": 0.8147181272506714,
"eai_open_web_math": 0.21052700281143188,
"eai_web_code": 0.44064104557037354
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "005.4",
"labels": {
"level_1": "General works, books and libraries, information sciences",
"level_2": "",
"level_3": "Computer programming"
}
},
"secondary": {
"code": "621.392",
"labels": {
"level_1": "Industrial arts, Technology, and Engineering",
"level_2": "Engineering",
"level_3": "Mechanical engineering and Machinery"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "3",
"label": "Apply"
},
"secondary": {
"code": "4",
"label": "Analyze"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "3",
"label": "Procedural"
},
"secondary": {
"code": "2",
"label": "Conceptual"
}
},
"document_type_v1": {
"primary": {
"code": "4",
"label": "Code/Software"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "0",
"label": "No Artifacts"
},
"secondary": {
"code": "0",
"label": "No Artifacts"
}
},
"missing_content": {
"primary": {
"code": "0",
"label": "No missing content"
},
"secondary": {
"code": "0",
"label": "No missing content"
}
},
"document_type_v2": {
"primary": {
"code": "8",
"label": "Documentation"
},
"secondary": {
"code": "3",
"label": "Academic Writing"
}
},
"reasoning_depth": {
"primary": {
"code": "4",
"label": "Advanced Reasoning"
},
"secondary": {
"code": "3",
"label": "Intermediate Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "4",
"label": "Highly Correct"
},
"secondary": {
"code": "3",
"label": "Mostly Correct"
}
},
"education_level": {
"primary": {
"code": "4",
"label": "Graduate/Expert Level"
},
"secondary": {
"code": "3",
"label": "Undergraduate Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
148,902,627,300,252,320 |
Discover the Fascinating Toronto Historical Weather Patterns
Have you ever wondered what the temperature was like in Toronto in the past? Are you interested in the historical weather records of this vibrant city? Look no further! In this article, we will take a deep dive into the climate history of Toronto, exploring the temperature patterns and weather events that have shaped the city over the years.
From scorching summers to icy winters, Toronto’s climate is diverse and ever-changing. By examining the historical weather records, we can gain a better understanding of how the temperature fluctuates throughout the year and how it has evolved over time. Whether you’re a weather enthusiast or simply curious about the past, this exploration of Toronto’s weather history is sure to captivate your interest.
By delving into the past, we can uncover fascinating insights into the temperature patterns that have shaped Toronto’s climate. Discover how the city has experienced heatwaves and cold snaps, hurricanes and blizzards, and everything in between. Through the lens of historical weather data, we can gain a new perspective on the city’s meteorological past, sparking our curiosity and appreciation for the forces of nature that have shaped Toronto into the vibrant metropolis it is today.
So, join us on this journey through time as we explore Toronto’s historical weather data. Delve into the temperature records, unravel the mysteries of past storms, and gain a deeper appreciation for the rich climate history of this remarkable city. Get ready to be captivated by the stories that lie within Toronto’s weather past!
Access Toronto’s Past Weather Records
If you want to dive into Toronto’s weather history and explore climate patterns, temperature trends, and other weather data from the past, you can access the city’s detailed past weather records.
These records provide valuable insights into the historical climate of Toronto, allowing you to analyze the temperature fluctuations, precipitation levels, and other weather phenomena that have occurred in the city over the years.
By accessing the past weather data, you can gather information about extreme weather events, such as heatwaves, cold snaps, or heavy snowfalls, and understand their frequency and intensity throughout Toronto’s history.
The past weather records also help you identify long-term climate patterns and changes, enabling you to observe any shifts in temperature trends, seasonal variations, or precipitation levels that may have occurred over the decades.
Whether you are a weather enthusiast, a researcher, or just curious about how the climate in Toronto has evolved over time, exploring the city’s past weather records can provide you with a wealth of fascinating information.
So, if you want to delve into Toronto’s weather history, access the vast data of past weather records, and uncover the secrets hidden within the city’s climate archives.
Explore Historical Weather in Toronto
Toronto, the capital city of the province of Ontario in Canada, has a rich history of weather records. By diving into the past, we can uncover fascinating information about Toronto’s climate over the years.
Temperature Records
One of the key aspects of Toronto’s historical weather data is the temperature records. These records span several decades and provide valuable insights into the city’s climatic patterns. By analyzing the temperature data, we can observe trends and fluctuations, including the hottest and coldest days in Toronto’s history.
Climate History
To understand the climate of Toronto, it is important to study its historical weather patterns. The climate history of the city reveals long-term trends, such as average temperatures throughout the year and seasonal variations. This information is crucial for predicting future climate changes and adapting to them.
The historical weather data of Toronto also allows us to compare the climate of different decades and analyze if any noticeable changes have occurred. This analysis can provide valuable insights into the impact of human activities and global climate change on Toronto’s climate.
Exploring the historical weather in Toronto is not only an interesting study of the past but also a useful tool for scientists, city planners, and residents alike. By understanding the weather patterns of the past, we can better prepare for the future and make informed decisions about our environment.
Uncover Toronto’s Weather History
Step into the past and immerse yourself in the historical climate records of Toronto, Canada. Delve into the treasure trove of weather data to uncover fascinating insights into the city’s weather patterns throughout the years.
With access to the temperature records dating back decades, you can gain a comprehensive understanding of Toronto’s weather history. Analyze the variations in temperature, observe trends, and discover how weather patterns have evolved over time.
By examining the historical weather data of Toronto, you can uncover a wealth of information about the city’s climate. Discover noteworthy climatic events, such as extreme heatwaves or unusually cold winters. Explore the patterns of rainfall and snowfall, and determine the averages for each season.
Uncovering Toronto’s weather history is not only intriguing but also provides valuable insights for various fields. Scientists and researchers can utilize this data to study climate change and its impact on the region. City planners can make informed decisions based on historical weather patterns, ensuring the city is prepared for future weather events.
Whether you’re a weather enthusiast, a student, or simply curious about Toronto’s past, diving into the historical weather records will offer a fascinating journey through time. Gain a deeper appreciation for the city’s climate and its impact on the lives of its residents.
So, embark on a journey of discovery and explore Toronto’s weather history. Unlock the secrets held within the past, and let the historical climate data paint a vivid picture of the city’s ever-changing weather.
Discover Toronto’s Climate Past
Toronto’s climate history is documented through the collection and analysis of historical weather data. These records provide valuable insights into the temperature and weather patterns that have shaped the city over time.
Exploring Toronto’s Weather Data
By delving into the historical weather records of Toronto, researchers can uncover fascinating information about the city’s climate throughout the years. This data includes details such as daily temperature readings, precipitation amounts, and other meteorological observations.
Studying the historical weather data allows us to understand Toronto’s climate trends, fluctuations, and overall patterns. This information is crucial for analyzing how the city’s climate has evolved over time, identifying potential climate change impacts, and making informed decisions for the future.
Historical Records Paint a Picture
Examining Toronto’s historical weather data provides a window into the past, allowing us to visualize what the city’s climate was like decades or even centuries ago. By comparing today’s climate with the past, we can gain a better appreciation for the changes that have occurred.
These records also help us identify any notable weather events that have impacted Toronto over the years, such as heatwaves, cold snaps, or significant storms. Understanding the history of extreme weather events assists us in preparing for future occurrences and developing strategies to mitigate their effects.
Year Temperature (°C) Precipitation (mm)
1900 14 800
1950 12 900
2000 15 700
Above is a snapshot of the historical weather data, showing the average temperature and precipitation levels for select years. This table gives us a quick comparison of Toronto’s climate in different time periods, revealing potential trends or shifts that may have occurred.
By studying Toronto’s climate history, we gain a deeper understanding of the city’s weather patterns, allowing us to adapt and plan for the future. The data collected over the years plays a vital role in guiding decision-making processes to ensure Toronto remains resilient in the face of climate change.
Weather Data for Toronto’s Historical Analysis
When conducting a climate or weather analysis, it is essential to have access to past data and records. In Toronto, historical weather data provides valuable insights into the city’s temperature patterns, allowing researchers and meteorologists to analyze and understand climate changes over time.
Importance of Historical Weather Data
Historical weather data is crucial for several reasons. It allows scientists to identify long-term climate trends, such as shifts in temperature patterns or the occurrence of extreme weather events. By comparing past weather data with current observations, researchers can determine if there are any significant changes, thereby providing valuable information for climate change projections and adaptation planning.
In Toronto, analyzing historical weather data assists in understanding how the city’s climate has evolved. This analysis can reveal if there have been any notable temperature fluctuations, such as increasing mean temperatures or variations in seasonal weather patterns. Meteorologists and researchers can then use this information to make more accurate predictions about future weather conditions.
Accessing Toronto’s Historical Weather Data
Accessing Toronto’s historical weather data can be done through various sources, such as the National Oceanic and Atmospheric Administration (NOAA), Environment Canada, or local meteorological offices. These organizations collect, store, and make available weather and climate data, including temperature records, precipitation levels, and atmospheric conditions.
Researchers and analysts can also utilize online platforms and databases that compile historical weather data into user-friendly formats, making it easier to analyze and visualize trends. These tools often provide options to filter the data by specific time periods, such as months or years, allowing for a more focused analysis of Toronto’s weather history.
Benefits of Analyzing Toronto’s Weather Data
Studying Toronto’s historical weather data offers several benefits. By analyzing past temperature records, researchers can identify if there are any climate-related challenges the city has faced in the past, such as heatwaves or prolonged cold spells. This information can help city planners and policymakers develop strategies to mitigate the impacts of extreme weather events.
Furthermore, historical weather data can support research on the link between climate change and human activities. By comparing historical weather patterns with industrial and population growth in the region, scientists can assess the influence of human factors on the city’s climate and build a better understanding of the long-term impacts of human activity on weather patterns.
Conclusion
Weather data plays a crucial role in understanding Toronto’s climate history. By analyzing past records, researchers can identify climate trends and deviations from normal weather patterns. This historical analysis provides valuable insights for climate change research, adaptation planning, and understanding the impacts of human activity on weather conditions. Access to accurate and reliable historical weather data is essential for any comprehensive weather analysis in Toronto.
Climate Trends in Toronto Over the Years
When it comes to studying climate in Toronto, historical weather data plays a crucial role. These records provide valuable insights into the climate history of the city, allowing us to understand the patterns and trends that have shaped Toronto’s weather over the years.
By analyzing the past temperature records, meteorologists and climatologists can identify long-term climate trends in Toronto. They can determine if the city has experienced any significant shifts in temperature, such as increasing or decreasing trends, and whether these changes are within the normal variability or indicative of more significant climate shifts.
Looking back at Toronto’s historical weather data, it becomes apparent that the city has indeed witnessed some notable trends in temperature. For example, there has been a clear warming trend over the past few decades, with average temperatures increasing steadily.
This warming trend aligns with the broader global climate change patterns observed around the world. However, it is important to note that climate variations at the regional level can deviate from global trends due to local factors.
Toronto’s climate history also highlights other interesting temperature patterns. For instance, there have been periods of warmer or cooler-than-average temperatures, often associated with specific weather patterns, such as El Niño or La Niña events.
In addition to temperature trends, historical weather data allows us to examine other climate variables, such as precipitation, humidity, and wind patterns. By studying these aspects, researchers can gain a more comprehensive understanding of Toronto’s climate and how it has evolved over time.
In conclusion, the historical weather records of Toronto provide a valuable resource for understanding the climate trends that have shaped the city’s weather patterns over the years. By analyzing this data, researchers can identify long-term temperature trends, explore variations, and gain insights into the factors influencing Toronto’s climate. This knowledge is crucial for predicting future climate changes and developing strategies to mitigate their impacts.
Understanding Toronto’s Weather Patterns
Toronto, the capital city of Ontario, Canada, has a diverse climate that varies throughout the year. The city has a long history of weather records, which provide valuable insights into its climate patterns.
Historical Weather Data
Toronto has a rich history of weather data collection, with records dating back several decades. These records include information on temperature, precipitation, wind speed, and other weather-related variables.
Analyzing historical weather data is crucial to understanding the climate patterns in Toronto. It allows meteorologists and climate scientists to identify trends and anomalies, such as long-term temperature changes or extreme weather events.
Temperature Trends
Temperature is one of the most important variables when it comes to understanding a region’s climate. Historical temperature data for Toronto reveals interesting patterns.
For example, the data may show a gradual increase or decrease in temperatures over time, indicating long-term climate change. It can also highlight seasonal temperature variations, with colder winters and hotter summers.
Extreme Weather Events
Another aspect of Toronto’s weather patterns that historical data helps to analyze is extreme weather events. These events can include heatwaves, blizzards, thunderstorms, and torrential rainfalls.
Studying historical records can reveal the frequency and intensity of these events, providing valuable information for understanding and preparing for future extreme weather occurrences.
Climate Change Implications
By examining the historical weather data in Toronto, scientists can gain insights into the possible implications of climate change. They can identify whether certain weather patterns are becoming more frequent or extreme, potentially indicating the influence of global warming.
Understanding Toronto’s weather patterns through historical weather data is essential for predicting future climate changes and developing strategies to mitigate their impacts. The availability of this data allows for informed decision-making when it comes to urban planning, infrastructure development, and disaster preparedness.
How to Access Toronto’s Weather History
If you are interested in exploring the historical weather data of Toronto, you have come to the right place. Toronto has a rich past when it comes to climate records and temperature fluctuations, and accessing this information can provide valuable insights into the city’s weather patterns over the years.
1. Online Databases and Websites
One of the easiest ways to access Toronto’s weather history is through online databases and websites. There are several reliable sources that offer past weather data for Toronto, including the Government of Canada’s official climate database and various meteorological websites. These platforms allow you to search for specific dates or periods and provide detailed information about temperature, precipitation, wind speeds, and other weather-related variables.
2. Local Weather Stations
Toronto is home to numerous weather stations that have been collecting data for many years. These stations are usually managed by meteorological organizations or academic institutions and are a great resource for accessing historical weather information. You can contact these stations directly to request specific data or inquire about their archives. They may have records dating back several decades or even a century, allowing you to delve deep into Toronto’s climate history.
3. Libraries and Archives
If you prefer a more hands-on approach, you can visit local libraries and archives in Toronto. Many libraries have sections dedicated to historical weather records, where you can find books, reports, and other publications that contain valuable climate data. Additionally, some archives may hold special collections of weather-related documents, such as handwritten climate observations or early weather maps. These resources can provide unique insights into Toronto’s weather history that may not be available online.
By using these methods, you can access Toronto’s weather history and gain a better understanding of how the climate has evolved over time. Whether you are a researcher, a weather enthusiast, or simply curious about the past, exploring the historical weather data of Toronto can be an exciting and educational experience.
Find Weather Data for Toronto
If you’re interested in exploring the historical weather records for Toronto, you’ve come to the right place. Toronto has a rich history of temperature fluctuations, making it an ideal location to study past climate patterns.
Why Historical Weather Data?
Studying historical weather data allows us to observe long-term climate trends and understand the changes that have occurred over time. By analyzing past weather records, we can gain insights into temperature variations, precipitation levels, and other climate patterns.
Accessing Toronto’s Climate Data
To access Toronto’s historical weather data, a variety of sources are available. One option is to visit the official meteorological organization’s website, where they often provide archived climate data. Another option is to use reputable online platforms that specialize in weather data, offering easy access to historical records for various locations, including Toronto. These platforms usually provide data in a user-friendly format, allowing you to navigate and explore the information efficiently.
Once you have found a reliable source, you can search for specific dates or periods of interest to explore the weather data for Toronto. This information can be useful for various purposes, such as conducting research, planning outdoor activities, or gaining a deeper understanding of the city’s climate history.
By delving into Toronto’s historical weather data, you can uncover fascinating insights into temperature fluctuations and climate patterns of the past. Whether you’re a weather enthusiast or simply curious, exploring weather records can be a captivating way to discover the city’s climatic history.
Access Toronto’s Weather Archives
When it comes to understanding the weather and climate in Toronto, there is no better resource than accessing the city’s past weather data. Toronto has a rich history, and looking at its historical weather records can provide valuable insights into the city’s temperature trends and climate patterns over time.
By accessing Toronto’s weather archives, you can explore a treasure trove of data that stretches back decades. Whether you are a weather enthusiast, a researcher, or simply curious about how the climate in Toronto has changed over the years, accessing this historical weather data can help answer your questions.
With this data, you can analyze the temperature variations, precipitation levels, and other weather patterns that have occurred in Toronto throughout history. Understanding past weather conditions can provide context for current climate trends, as well as offer valuable information for future predictions.
Accessing Toronto’s weather archives allows you to dive deep into the city’s weather history. You can explore specific time periods, compare different years, or track the long-term climate changes that have taken place. Whether you are interested in the hottest summer on record or the coldest winter in history, the data is there for you to discover.
By analyzing the historical weather data, you can also gain a better understanding of how climate change is impacting Toronto. The past records can help illustrate the ways in which temperatures, precipitation, and other weather factors have shifted over time, providing a valuable perspective on the city’s climate future.
So, whether you are a weather enthusiast, a climate scientist, or simply someone interested in the history of Toronto’s weather, access to the city’s weather archives is a valuable resource. Explore the past, analyze the data, and gain a deeper understanding of Toronto’s weather and climate.
Explore Toronto’s Historical Climate Data
When it comes to understanding the weather and climate in Toronto, knowing the city’s history is key. By exploring Toronto’s historical climate data, you can gain valuable insights into the past weather patterns and temperature records of the city.
The Importance of Climate History
Understanding the past climate of Toronto is essential for various reasons. It helps scientists and researchers analyze trends, identify patterns, and make predictions for future climate changes. Moreover, historical climate data is also crucial for urban planning, infrastructure design, and assessing the impact of climate change on the city.
By studying the historical weather patterns in Toronto, researchers can determine how temperatures have fluctuated over the years, identify any long-term trends, and compare them with current weather conditions. This analysis provides an invaluable perspective on the climate changes that have occurred.
Discovering Toronto’s Weather Records
Toronto has a rich collection of historical climate data that spans over a century. These records include information on temperature, precipitation, wind speed, and other meteorological factors. The data is collected and maintained by various organizations such as Environment Canada, the Royal Ontario Museum, and local weather stations.
By tapping into these vast archives of historical climate data, researchers and weather enthusiasts can explore Toronto’s weather records and gain a deeper understanding of the city’s climate history. This data can be used to analyze long-term climate patterns, compare current weather conditions with past records, and even make predictions for future climate trends.
In conclusion, exploring Toronto’s historical climate data enables us to dive into the city’s weather records and gain insights into its past climate conditions. By analyzing this information, we can better comprehend the impact of climate change on Toronto and make informed decisions for the future.
Unlocking Toronto’s Weather Secrets
The climate of Toronto has a rich history, and understanding its past weather patterns can provide valuable insights into the city’s climate trends. Unlocking Toronto’s weather secrets involves delving into historical weather data to uncover records of temperature, precipitation, and other meteorological phenomena that have occurred in the past.
By analyzing the historical weather data, researchers, meteorologists, and weather enthusiasts can gain a better understanding of how Toronto’s climate has changed over time. This data can reveal long-term climate trends, such as shifts in average temperature or precipitation levels, as well as extreme weather events that may have occurred in the city’s past.
The historical weather data for Toronto can be accessed from various sources, including government weather agencies, research institutions, and online databases. These sources provide a wealth of information, including daily weather observations, monthly averages, and even data collected from weather stations in the city’s distant past.
Studying Toronto’s weather records allows us to compare current weather conditions to those of the past. We can see how temperatures have fluctuated over decades or centuries and identify any significant changes. This knowledge is essential for understanding the impact of climate change on the city and making informed decisions regarding its future.
Temperature is one of the essential factors of climate, and historical temperature data is particularly valuable for studying Toronto’s climate history. By examining temperature records, we can identify patterns, anomalies, and significant events, such as heatwaves or cold snaps, that have shaped the city’s weather.
Unlocking Toronto’s weather secrets through the exploration of historical data provides us with valuable insights into the city’s climate and its evolution over the years. It allows us to understand how Toronto’s weather has shaped its past and how it may continue to influence its future.
Obtaining Past Weather Reports for Toronto
If you are interested in exploring the climate of Toronto, you can easily obtain historical weather records and data for the city. These records provide valuable information about the past weather conditions, temperature, and other relevant details.
To access the historical weather reports for Toronto, you can visit various websites that offer this data. One popular source is the National Weather Service website, which provides comprehensive weather information for different locations.
Weather Data Websites
There are several websites dedicated to compiling and providing historical weather data. These websites collect information from various sources, including weather stations, and make it accessible to the public.
• Weather Underground – This website offers an extensive collection of historical weather data, including temperature, precipitation, wind speed, and more. You can search for Toronto’s weather history by entering the city’s name and selecting the desired date range.
• AccuWeather – AccuWeather provides past weather reports for many cities around the world, including Toronto. You can search for specific dates or periods to retrieve detailed weather information.
• Environment and Climate Change Canada – This government website provides climate data for various regions in Canada, including Toronto. You can access historical weather records, climate summaries, and climate normals for different time periods.
Using Historical Weather Data
Once you have obtained the past weather reports for Toronto, you can analyze the data to gain insights into the city’s climate patterns. You can examine temperature trends over the years, seasonal variations, and extreme weather events.
This historical weather data can be useful for various purposes, such as planning outdoor activities, studying climate change, or conducting research. It allows you to compare current weather conditions with past records and identify any significant changes or anomalies.
By understanding Toronto’s weather history, you can make more informed decisions and adapt to the city’s climate conditions effectively.
Dig into Toronto’s Weather Documents
The past weather records provide valuable insights into the historical weather patterns of Toronto. These records contain data related to temperature, precipitation, wind speed, and other meteorological phenomena, offering a glimpse into the weather history of the city.
In order to understand the climate and weather trends in Toronto, it is essential to analyze and interpret the historical weather data. The records can help researchers and meteorologists identify patterns, study the impact of climate change, and make predictions about future weather conditions.
By examining these documents, we can discover interesting facts about the weather in Toronto throughout history. For example, we can learn about extreme temperature fluctuations, precipitation patterns, and notable weather events that have occurred in the city.
Studying the historical weather records allows us to gain a deeper appreciation for the weather patterns that have shaped Toronto over the years. It offers a unique perspective on the city’s climate and provides a basis for understanding how it has evolved over time.
Whether you are a weather enthusiast, a researcher, or simply curious about Toronto’s weather history, delving into these weather documents can provide a fascinating glimpse into the rich meteorological history of the city.
Retrieve Toronto’s Weather Data
If you’re interested in finding out information about Toronto’s weather in the past, you can retrieve historical weather data to get a glimpse into the climate records of the city. By accessing this data, you can explore temperature changes over the years and gain insights into Toronto’s weather history.
To retrieve Toronto’s weather data, you can consult various sources such as meteorological organizations or online platforms dedicated to weather records. These sources collect and archive data from weather stations located in Toronto, providing a comprehensive overview of the city’s climate.
By accessing past weather data, you can analyze temperature patterns, identify trends, and compare different time periods. This can be useful in understanding how Toronto’s climate has changed over time and how it may continue to evolve in the future.
Date Temperature (°C)
January 1, 2022 -2
January 2, 2022 -5
January 3, 2022 -7
The table above showcases a sample of Toronto’s historical weather data, providing the date and corresponding temperature in Celsius for a few selected days. By retrieving a complete dataset, you can delve deeper into Toronto’s weather history and explore seasonal variations, extreme weather events, and long-term climate trends.
Whether you’re a weather enthusiast, researcher, or simply curious about Toronto’s climate records, accessing historical weather data can offer valuable insights into the city’s weather patterns in the past. It allows you to better understand the overall climate of Toronto and appreciate the uniqueness of its weather throughout history.
Access Toronto’s Weather Records for Research
If you are conducting research on weather in Toronto, accessing past weather records can provide valuable insights. Toronto has a rich history of climate data, including temperature records that span several decades.
By analyzing historical weather data, researchers can gain a deeper understanding of Toronto’s climate patterns and how they have changed over time. This information can be used to study the impacts of climate change, identify trends, and make informed predictions about future weather patterns in the city.
Temperature Records
One of the key aspects of weather data that researchers focus on is temperature. Toronto’s historical temperature records are available for analysis and can be accessed for research purposes. These records include daily temperature measurements taken at various locations throughout the city.
Researchers can examine the temperature records to identify trends and patterns in Toronto’s climate. For example, they can analyze how average temperatures have changed over time, identify the hottest and coldest days on record, and study the frequency of extreme weather events such as heatwaves or cold snaps.
Accessing the Data
To access Toronto’s weather records, researchers can utilize various online platforms and databases. The Environment and Climate Change Canada provides a comprehensive database of historical weather data, including records for Toronto.
By navigating through this database, researchers can select the desired time period and location to obtain the weather records they need. The data is typically available in a downloadable format, allowing for further analysis and research.
Accessing Toronto’s weather records for research purposes can help scientists, meteorologists, and climate researchers in their efforts to understand the city’s climate and how it is changing. By analyzing historical weather data, researchers can contribute to ongoing studies on climate change and help shape future strategies for dealing with its impacts.
What Can You Learn from Toronto’s Weather History
Exploring Toronto’s historical weather data can provide valuable insights into the climate patterns and temperature fluctuations experienced in the past. By analyzing the records of the weather conditions, you can gain a deeper understanding of how the weather has evolved over time.
Studying the past weather data allows you to identify trends and patterns that can help predict and forecast future weather conditions. By examining historical temperature records, you can observe long-term climate changes and variations in seasonal temperatures. This information can be useful for researchers, scientists, and meteorologists studying climate change and its impact on the region.
The historical weather data can also reveal interesting facts about Toronto’s climatic history. By examining records of extreme weather events, such as heatwaves, cold snaps, or heavy snowfall, you can gain insights into the city’s resilience and adaptability to different weather conditions. Additionally, these records can document significant storms or natural disasters that have shaped the city’s history.
Understanding Toronto’s weather history can also provide context for current weather patterns and help individuals and communities prepare for potential weather hazards. By analyzing past weather data, you can identify recurring weather patterns, such as seasonal rainfall or periods of drought. This knowledge can guide decision-making processes related to agriculture, urban planning, and disaster preparedness.
Overall, exploring Toronto’s historical weather data offers a glimpse into the city’s past and provides valuable information about the region’s climate and weather patterns. By understanding the past, we can better prepare for the future and make informed decisions based on historical records and data.
Explore Toronto’s Extreme Weather Events
In order to understand the climate of Toronto, it is crucial to analyze historical weather data. By examining past temperature patterns and extreme weather events, we can gain valuable insights into the city’s climate history.
Temperature Data
Temperature data is a vital component of studying past weather conditions in Toronto. By analyzing temperature records from previous years, meteorologists and climatologists can identify trends and patterns in the city’s climate.
Extreme temperature events, such as heatwaves and cold snaps, can have a significant impact on public health and infrastructure. Understanding these events and their frequency helps city planners and policymakers develop strategies to mitigate their effects.
Climate History
Toronto’s climate has been shaped by a combination of natural factors and human influences. Historical weather data provides us with a detailed account of how the city’s climate has evolved over time.
Studying climate history helps us understand how Toronto’s weather patterns have changed and enables us to make predictions about future climate scenarios. This information is essential for developing strategies to adapt to climate change and minimize its impact on the city and its residents.
In conclusion, exploring Toronto’s extreme weather events through historical weather data is crucial for understanding the city’s climate and developing strategies to mitigate the effects of climate change. By analyzing temperature data and studying climate history, we can make informed decisions to protect the city and its inhabitants from the impacts of extreme weather events.
Uncover Toronto’s Seasonal Weather Patterns
By studying historical weather data, we can gain valuable insights into Toronto’s seasonal weather patterns. Looking back at past records, we can observe the changes in temperature throughout the year, which can help us better understand Toronto’s climate.
Toronto’s weather data history provides us with a wealth of information, allowing us to examine how temperature fluctuates over time. By analyzing this data, we can identify trends and patterns that emerge during different seasons.
For example, we may discover that Toronto experiences hot and humid summers, with temperatures often exceeding 30 degrees Celsius. In contrast, winters can be bitterly cold, with temperatures dropping below freezing and heavy snowfall common. Spring and autumn tend to have milder temperatures as the city transitions between the extremes of summer and winter.
Having access to historical weather data also enables us to compare current weather conditions to the long-term averages. We can see if the temperature is higher or lower than usual for a specific time of year, giving us insight into the changing climate patterns in Toronto.
Furthermore, historical weather records can help us plan for the future. By examining the data, we can anticipate potential weather patterns and trends, allowing us to make informed decisions about seasonal activities and events in Toronto.
Season Average Temperature
Spring 10°C – 20°C
Summer 25°C – 35°C
Autumn 10°C – 20°C
Winter -10°C – 0°C
As shown in the table above, Toronto experiences significant temperature variations across the seasons. This range of temperatures influences the local environment and affects various aspects of daily life, such as clothing choices, outdoor activities, and even the growth of plants and crops.
In summary, exploring Toronto’s historical weather data provides us with a fascinating glimpse into the city’s climate history. By analyzing past records, we can uncover seasonal weather patterns, understand temperature fluctuations, and anticipate future trends. This information is crucial for planning and adapting to Toronto’s diverse weather conditions throughout the year.
Discover Toronto’s Weather Anomalies
In the vast climate history of Toronto, there have been numerous weather anomalies that have left their mark in the historical records. These anomalies, characterized by extreme variations in temperature and weather patterns, offer a glimpse into the unpredictable and ever-changing nature of Toronto’s climate.
Record-breaking Temperatures
One of the most prominent anomalies in Toronto’s weather history is the occurrence of record-breaking temperatures. Throughout the past decades, there have been instances of both scorching heat and bitter cold that have surpassed previous records. These extreme temperature anomalies are a reminder of the city’s diverse climate and its susceptibility to occasional weather extremes.
Unusual Weather Patterns
In addition to extreme temperatures, Toronto has experienced a variety of unusual weather patterns throughout its history. From unexpected snowstorms in spring to unseasonably warm winters, these anomalies remind us that weather systems can sometimes defy typical seasonal expectations. These peculiar weather patterns have often caught Torontonians off guard, highlighting the need for flexibility and adaptability in the face of changing climate conditions.
Exploring the historical weather data of Toronto not only provides valuable insights into the city’s past climate, but also serves as a reminder of the dynamic nature of weather and its impact on our daily lives. It encourages us to appreciate the unpredictability and uniqueness of Toronto’s climate and the need for ongoing analysis and adaptation to ensure the city’s resilience in the face of future weather anomalies.
Study Toronto’s Long-term Climate Trends
Temperature is one of the key factors when it comes to understanding and studying a city’s climate. In the case of Toronto, the past historical records provide valuable insights into the city’s climate trends over the years.
Toronto has a rich history, and so do its climate records. A look back into the past can help us understand how the temperature in Toronto has changed over time. Historical climate data is a valuable resource for scientists, researchers, and anyone interested in studying the city’s climate history.
By analyzing the historical temperature data, it is possible to identify long-term trends in Toronto’s climate. This information can be used to predict future climate patterns, assess the impact of climate change, and plan for the future.
The Importance of Historical Climate Data
Historical climate data provides a baseline for understanding current and future climate trends. By analyzing past records, scientists can identify patterns and anomalies in temperature, precipitation, and other climate variables.
For example, historical climate data can help identify trends such as rising temperatures over time, increasing frequency of extreme weather events, or shifts in the timing of seasons. This information is crucial for understanding the impacts of climate change on a local and global scale.
Exploring Toronto’s Climate History
Toronto’s climate history dates back to the early 19th century when the city’s first weather stations were established. These stations collected valuable meteorological data, including temperature, for over a century.
By analyzing this historical climate data, researchers have gained insights into how Toronto’s climate has evolved over time. For example, they have observed an overall increase in average temperatures, changes in precipitation patterns, and shifts in the duration of seasons.
Studying Toronto’s long-term climate trends provides us with a better understanding of how the city’s climate has changed and how it might continue to change in the future. This knowledge is essential for making informed decisions about climate adaptation and mitigation strategies.
In conclusion, historical climate data plays a crucial role in studying Toronto’s long-term climate trends. By analyzing temperature records and other climate variables, researchers can gain valuable insights into how the city’s climate has evolved over time. This knowledge is essential for understanding the impacts of climate change and planning for the future.
Analyze Toronto’s Weather Data for Planning
Exploring the history of weather conditions is crucial when planning any activities or events. By analyzing the historical weather records in Toronto, one can gain valuable insights into the climatic patterns and temperature variations observed in the past.
Understanding Toronto’s Climate
Toronto is known for its diverse climate, experiencing all four seasons throughout the year. The city’s climate is influenced by its proximity to Lake Ontario and its location within the North American continent. The summers in Toronto are generally warm and humid, while the winters can be cold, with intermittent snowfall.
Analyzing Temperature Records
Temperature is an essential factor to consider when planning outdoor activities or events in Toronto. By accessing and analyzing the historical weather data, one can determine the average temperature at a specific time of the year. This information can help in making informed decisions regarding event scheduling and preparation.
Date Temperature (°C) Weather
January 1, 2010 -3 Snowy
July 15, 2015 28 Sunny
October 10, 2018 10 Cloudy
March 20, 2022 5 Rainy
By studying the temperature records from previous years, one can identify the hottest and coldest months in Toronto and plan accordingly. For example, if you are organizing an outdoor event in the summer, it would be wise to choose a date in July or August when the average temperature is at its peak. On the other hand, if you prefer milder weather, the months of May or September could be ideal.
Weather conditions can greatly impact the success of outdoor activities or events. Analyzing Toronto’s historical weather data allows for better planning, ensuring that appropriate measures can be taken to make the most of the weather conditions and enhance the overall experience.
Understand the Impact of Weather on Toronto’s History
The climate and weather conditions in Toronto have played a crucial role in shaping its historical events and development. By analyzing the historical weather data and records, we can gain valuable insights into how temperature fluctuations and extreme weather events have influenced the city’s growth.
Historical weather records allow us to track the changing climate patterns in Toronto over the years. We can observe trends in average temperatures, rainfall, and snowfall, providing us with an understanding of the city’s climatic conditions in different time periods.
Temperature data is particularly important when studying the impact of weather on Toronto’s history. Extreme heatwaves or cold spells have had significant consequences on the city and its residents. For example, heatwaves can lead to health risks and increased energy demand, while severe cold weather can disrupt transportation systems and affect daily activities.
By examining the historical weather data, we can also identify how weather events have influenced specific historical events in Toronto. For instance, severe storms or heavy snowfall may have impacted historical battles, construction projects, or transportation routes.
Understanding the historical weather patterns helps us appreciate the resilience and adaptability of Toronto’s residents throughout its history. It allows us to recognize the challenges they faced and how they have shaped the city’s infrastructure and resources.
In conclusion, exploring Toronto’s climate and historical weather data gives us a deeper understanding of its history. By studying temperature fluctuations, extreme weather events, and their impact on the city, we can gain valuable insights into the challenges and triumphs that have shaped Toronto into the city it is today.
Where to Find Toronto’s Weather Historical Data
If you are looking for past weather data in Toronto, there are several reliable sources where you can access the historical weather records. These records can provide valuable insights into the temperature trends and weather patterns over the years.
1. Environment Canada
Environment Canada is the official source for weather data in Canada. Their website provides access to historical weather information for various locations, including Toronto. You can search for specific dates and download the data in a convenient format.
2. Weather Underground
Weather Underground is a popular weather website that offers historical weather data for many cities around the world, including Toronto. You can explore their extensive database and find detailed temperature records for specific dates in the past.
3. National Climatic Data Center
The National Climatic Data Center (NCDC) is a resource maintained by the National Oceanic and Atmospheric Administration (NOAA) in the United States. They offer an extensive collection of weather data from different countries, including Canada. You can search for Toronto’s historical weather data on their website.
By accessing these reliable sources of historical weather data, you can delve into Toronto’s weather history and analyze temperature trends and patterns over the years. Whether you are a weather enthusiast, researcher, or simply curious about the past weather conditions in Toronto, these resources can provide valuable information.
Online Resources for Toronto’s Weather History
Exploring the climate and weather history of Toronto can provide valuable insights into the city’s past. Fortunately, there are several online resources that offer comprehensive data and records on historical weather conditions in Toronto.
1. Environment and Climate Change Canada
The Environment and Climate Change Canada website provides a wealth of historical weather information for Toronto. Through their Climate Data Online tool, you can access historical climate data, including temperature records, precipitation levels, and wind speeds. The data is available for different time periods, allowing you to analyze how Toronto’s weather patterns have changed over the years.
2. Toronto Weather Records
Another useful resource is the Toronto Weather Records website, which is dedicated to documenting the city’s weather history. It offers a variety of information, such as temperature records for each month, extreme weather events, and interesting weather facts about Toronto. The website also includes interactive charts and graphs to visualize the data and make comparisons between different years.
By utilizing these online resources, you can delve into Toronto’s historical weather data and gain a better understanding of the city’s climate patterns. Whether you are researching for a project, planning outdoor activities, or simply curious about Toronto’s weather history, these resources can provide valuable insights.
Discover Toronto’s Weather History Archives
Toronto’s climate records hold a wealth of information about the city’s past weather patterns. These historical records provide an insight into the temperature and weather conditions that Toronto has experienced over the years.
Exploring the weather history of Toronto allows us to understand how the climate has changed and evolved over time. It helps us to analyze trends and patterns, and to gain a deeper understanding of the city’s past.
The temperature data from the past can be invaluable for researchers, meteorologists, and climate scientists. It provides a baseline for studying climate change and predicting future weather patterns.
Toronto’s historical weather archives not only include temperature records but also information about precipitation, wind speed, and other weather variables. Accessing these archives allows us to delve into the rich history and evolution of Toronto’s weather and climate.
By studying the weather history of Toronto, we can gain a greater appreciation for the city’s resilience in the face of extreme weather events. It helps us to understand how Toronto has adapted and prepared for various weather conditions throughout its history.
Whether you’re a weather enthusiast, a researcher, or simply curious about Toronto’s past, exploring the city’s weather history archives is a fascinating journey into the city’s climate and meteorological past.
So, dive into the historical records and uncover the facts and figures that tell the story of Toronto’s weather throughout the years.
Discover Toronto’s weather history archives and unlock the secrets of the city’s past!
Access Toronto’s Weather Reports from the Past
Exploring the historical weather data of Toronto can provide valuable insights into the city’s climate over time. By accessing the past weather records, you can analyze trends in temperature and weather patterns, giving you a better understanding of how Toronto’s climate has evolved.
Why Access Historical Weather Data?
Accessing historical weather data is essential for various purposes. Researchers, scientists, and meteorologists often rely on this data to study climate change, identify long-term weather patterns, and make predictions for the future. Additionally, historical weather data is valuable for city planning, building design, and even recreational activities.
What Can You Find in Toronto’s Historical Weather Reports?
Toronto’s historical weather reports contain a wealth of information, including daily temperature records, precipitation levels, wind speed, cloud cover, and more. These reports provide a comprehensive overview of past weather conditions in the city, allowing you to explore specific dates or periods of interest.
Temperature: The historical weather reports include details on daily high and low temperatures, allowing you to compare current weather conditions with those in the past. This information can be useful for detecting temperature trends and understanding climate variations in Toronto.
Weather Conditions: The reports also cover various weather conditions, such as sunny, cloudy, rainy, or snowy days. By analyzing this data, you can identify patterns in weather types and gain insights into Toronto’s seasonal changes.
Precipitation: Precipitation data in historical weather reports can help you understand rainfall patterns, detect drought or flood periods, and analyze how precipitation levels have changed over time.
Exploring Toronto’s historical weather data is an exciting way to dive into the city’s climate history. Whether you are a climate enthusiast, a researcher, or someone planning outdoor activities, accessing this data can provide valuable insights and a deeper appreciation for Toronto’s weather.
|
{
"url": "https://historyoftoronto.ca/blog/discover-the-fascinating-toronto-historical-weather-patterns",
"source_domain": "historyoftoronto.ca",
"snapshot_id": "CC-MAIN-2024-38",
"warc_metadata": {
"Content-Length": "115270",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:7SUZIH4ZXJF5GSGJCX5IAH76QNBJICPG",
"WARC-Concurrent-To": "<urn:uuid:f9314c87-4677-43d7-a79f-d84dd52a4fec>",
"WARC-Date": "2024-09-15T12:47:50Z",
"WARC-IP-Address": "104.21.22.236",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:WPDQV4NNG266LHEA7B4USF6APUIJH5SV",
"WARC-Record-ID": "<urn:uuid:db87d0b8-eff6-4640-baf1-f8c228d00e70>",
"WARC-Target-URI": "https://historyoftoronto.ca/blog/discover-the-fascinating-toronto-historical-weather-patterns",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:3a5469cf-4334-4264-a596-2027474c8a35>"
},
"warc_info": "isPartOf: CC-MAIN-2024-38\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for September 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-37\r\nsoftware: Apache Nutch 1.20 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
61,
62,
406,
407,
814,
815,
1301,
1302,
1633,
1634,
1672,
1673,
1869,
1870,
2101,
2102,
2321,
2322,
2554,
2555,
2779,
2780,
2950,
2951,
2989,
2990,
3197,
3198,
3218,
3219,
3543,
3544,
3560,
3561,
3877,
3878,
4157,
4158,
4461,
4462,
4496,
4497,
4724,
4725,
4971,
4972,
5272,
5273,
5629,
5630,
5904,
5905,
6117,
6118,
6150,
6151,
6374,
6375,
6408,
6409,
6688,
6689,
6992,
6993,
7028,
7029,
7309,
7310,
7622,
7623,
7664,
7676,
7688,
7700,
7701,
7976,
7977,
8282,
8283,
8330,
8331,
8629,
8630,
8668,
8669,
9085,
9086,
9484,
9485,
9529,
9530,
9894,
9895,
10254,
10255,
10300,
10301,
10680,
10681,
11062,
11063,
11074,
11075,
11558,
11559,
11600,
11601,
11872,
11873,
12237,
12238,
12504,
12505,
12738,
12739,
12990,
12991,
13286,
13287,
13752,
13753,
13794,
13795,
14003,
14004,
14028,
14029,
14240,
14241,
14483,
14484,
14503,
14504,
14676,
14677,
14898,
14899,
14922,
14923,
15121,
15122,
15309,
15310,
15338,
15339,
15617,
15618,
15949,
15950,
15990,
15991,
16297,
16298,
16331,
16332,
16794,
16795,
16821,
16822,
17303,
17304,
17330,
17331,
17850,
17851,
18172,
18173,
18203,
18204,
18430,
18431,
18460,
18461,
18732,
18733,
18766,
18767,
19271,
19272,
19585,
19586,
19885,
19886,
19920,
19921,
20232,
20233,
20546,
20547,
20854,
20855,
21207,
21208,
21532,
21533,
21826,
21827,
21869,
21870,
22119,
22120,
22154,
22155,
22500,
22501,
22805,
22806,
22844,
22845,
23184,
23185,
23554,
23555,
23857,
23858,
23894,
23895,
24243,
24244,
24606,
24607,
24946,
24947,
25299,
25300,
25618,
25619,
25909,
25910,
25953,
25954,
26204,
26205,
26446,
26447,
26469,
26470,
26682,
26683,
26952,
27152,
27402,
27403,
27433,
27434,
27671,
27672,
27944,
27945,
28082,
28083,
28120,
28121,
28392,
28393,
28690,
28691,
28953,
28954,
29222,
29223,
29447,
29448,
29480,
29481,
29787,
29788,
30082,
30083,
30335,
30336,
30358,
30377,
30396,
30415,
30416,
30747,
30748,
31087,
31088,
31134,
31135,
31352,
31353,
31661,
31662,
31682,
31683,
31976,
31977,
32290,
32291,
32310,
32311,
32547,
32548,
32788,
32789,
33147,
33148,
33198,
33199,
33483,
33484,
33881,
33882,
34293,
34294,
34708,
34709,
35013,
35014,
35055,
35056,
35283,
35284,
35301,
35302,
35532,
35533,
35792,
35793,
35809,
35810,
36011,
36012,
36306,
36307,
36689,
36690,
36734,
36735,
36992,
36993,
37225,
37226,
37587,
37588,
37864,
37865,
38106,
38107,
38134,
38153,
38172,
38191,
38210,
38211,
38505,
38506,
38885,
38886,
38923,
38924,
39236,
39237,
39266,
39267,
39651,
39652,
39677,
39678,
40138,
40139,
40562,
40563,
40604,
40605,
40830,
40831,
41131,
41132,
41371,
41372,
41414,
41415,
41646,
41647,
41937,
41938,
41974,
41975,
42193,
42194,
42471,
42472,
42755,
42756,
43121,
43122,
43166,
43167,
43425,
43426,
43458,
43459,
43784,
43785,
43815,
43816,
44147,
44148,
44178,
44203,
44226,
44253,
44276,
44277,
44675,
44676,
44958,
44959,
45013,
45014,
45317,
45318,
45587,
45588,
45954,
45955,
46225,
46226,
46489,
46490,
46816,
46817,
46865,
46866,
47116,
47117,
47139,
47140,
47396,
47397,
47420,
47421,
47670,
47671,
47704,
47705,
48021,
48022,
48355,
48356,
48403,
48404,
48644,
48645,
48686,
48687,
49086,
49087,
49114,
49115,
49514,
49515,
49839,
49840,
49884,
49885,
50113,
50114,
50329,
50330,
50531,
50532,
50804,
50805,
51067,
51068,
51279,
51280,
51414,
51415,
51502,
51503,
51550,
51551,
51836,
51837,
51873,
51874,
52230,
52231,
52290,
52291,
52605,
52606,
52890,
52891,
53125,
53126,
53327,
53328
],
"line_end_idx": [
61,
62,
406,
407,
814,
815,
1301,
1302,
1633,
1634,
1672,
1673,
1869,
1870,
2101,
2102,
2321,
2322,
2554,
2555,
2779,
2780,
2950,
2951,
2989,
2990,
3197,
3198,
3218,
3219,
3543,
3544,
3560,
3561,
3877,
3878,
4157,
4158,
4461,
4462,
4496,
4497,
4724,
4725,
4971,
4972,
5272,
5273,
5629,
5630,
5904,
5905,
6117,
6118,
6150,
6151,
6374,
6375,
6408,
6409,
6688,
6689,
6992,
6993,
7028,
7029,
7309,
7310,
7622,
7623,
7664,
7676,
7688,
7700,
7701,
7976,
7977,
8282,
8283,
8330,
8331,
8629,
8630,
8668,
8669,
9085,
9086,
9484,
9485,
9529,
9530,
9894,
9895,
10254,
10255,
10300,
10301,
10680,
10681,
11062,
11063,
11074,
11075,
11558,
11559,
11600,
11601,
11872,
11873,
12237,
12238,
12504,
12505,
12738,
12739,
12990,
12991,
13286,
13287,
13752,
13753,
13794,
13795,
14003,
14004,
14028,
14029,
14240,
14241,
14483,
14484,
14503,
14504,
14676,
14677,
14898,
14899,
14922,
14923,
15121,
15122,
15309,
15310,
15338,
15339,
15617,
15618,
15949,
15950,
15990,
15991,
16297,
16298,
16331,
16332,
16794,
16795,
16821,
16822,
17303,
17304,
17330,
17331,
17850,
17851,
18172,
18173,
18203,
18204,
18430,
18431,
18460,
18461,
18732,
18733,
18766,
18767,
19271,
19272,
19585,
19586,
19885,
19886,
19920,
19921,
20232,
20233,
20546,
20547,
20854,
20855,
21207,
21208,
21532,
21533,
21826,
21827,
21869,
21870,
22119,
22120,
22154,
22155,
22500,
22501,
22805,
22806,
22844,
22845,
23184,
23185,
23554,
23555,
23857,
23858,
23894,
23895,
24243,
24244,
24606,
24607,
24946,
24947,
25299,
25300,
25618,
25619,
25909,
25910,
25953,
25954,
26204,
26205,
26446,
26447,
26469,
26470,
26682,
26683,
26952,
27152,
27402,
27403,
27433,
27434,
27671,
27672,
27944,
27945,
28082,
28083,
28120,
28121,
28392,
28393,
28690,
28691,
28953,
28954,
29222,
29223,
29447,
29448,
29480,
29481,
29787,
29788,
30082,
30083,
30335,
30336,
30358,
30377,
30396,
30415,
30416,
30747,
30748,
31087,
31088,
31134,
31135,
31352,
31353,
31661,
31662,
31682,
31683,
31976,
31977,
32290,
32291,
32310,
32311,
32547,
32548,
32788,
32789,
33147,
33148,
33198,
33199,
33483,
33484,
33881,
33882,
34293,
34294,
34708,
34709,
35013,
35014,
35055,
35056,
35283,
35284,
35301,
35302,
35532,
35533,
35792,
35793,
35809,
35810,
36011,
36012,
36306,
36307,
36689,
36690,
36734,
36735,
36992,
36993,
37225,
37226,
37587,
37588,
37864,
37865,
38106,
38107,
38134,
38153,
38172,
38191,
38210,
38211,
38505,
38506,
38885,
38886,
38923,
38924,
39236,
39237,
39266,
39267,
39651,
39652,
39677,
39678,
40138,
40139,
40562,
40563,
40604,
40605,
40830,
40831,
41131,
41132,
41371,
41372,
41414,
41415,
41646,
41647,
41937,
41938,
41974,
41975,
42193,
42194,
42471,
42472,
42755,
42756,
43121,
43122,
43166,
43167,
43425,
43426,
43458,
43459,
43784,
43785,
43815,
43816,
44147,
44148,
44178,
44203,
44226,
44253,
44276,
44277,
44675,
44676,
44958,
44959,
45013,
45014,
45317,
45318,
45587,
45588,
45954,
45955,
46225,
46226,
46489,
46490,
46816,
46817,
46865,
46866,
47116,
47117,
47139,
47140,
47396,
47397,
47420,
47421,
47670,
47671,
47704,
47705,
48021,
48022,
48355,
48356,
48403,
48404,
48644,
48645,
48686,
48687,
49086,
49087,
49114,
49115,
49514,
49515,
49839,
49840,
49884,
49885,
50113,
50114,
50329,
50330,
50531,
50532,
50804,
50805,
51067,
51068,
51279,
51280,
51414,
51415,
51502,
51503,
51550,
51551,
51836,
51837,
51873,
51874,
52230,
52231,
52290,
52291,
52605,
52606,
52890,
52891,
53125,
53126,
53327,
53328,
53622
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 53622,
"ccnet_original_nlines": 494,
"rps_doc_curly_bracket": 0,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.3466118276119232,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.0016237299423664808,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.1293569952249527,
"rps_doc_frac_unique_words": 0.11567925661802292,
"rps_doc_mean_word_length": 5.699565887451172,
"rps_doc_num_sentences": 387,
"rps_doc_symbol_to_word_ratio": 0,
"rps_doc_unigram_entropy": 5.315335750579834,
"rps_doc_word_count": 7832,
"rps_doc_frac_chars_dupe_10grams": 0.024350900202989578,
"rps_doc_frac_chars_dupe_5grams": 0.29232287406921387,
"rps_doc_frac_chars_dupe_6grams": 0.1900356262922287,
"rps_doc_frac_chars_dupe_7grams": 0.11012791842222214,
"rps_doc_frac_chars_dupe_8grams": 0.06344228237867355,
"rps_doc_frac_chars_dupe_9grams": 0.029615359380841255,
"rps_doc_frac_chars_top_2gram": 0.04036828875541687,
"rps_doc_frac_chars_top_3gram": 0.03387172892689705,
"rps_doc_frac_chars_top_4gram": 0.011335380375385284,
"rps_doc_books_importance": -2804.990966796875,
"rps_doc_books_importance_length_correction": -2804.990966796875,
"rps_doc_openwebtext_importance": -1824.1649169921875,
"rps_doc_openwebtext_importance_length_correction": -1824.1649169921875,
"rps_doc_wikipedia_importance": -1763.983642578125,
"rps_doc_wikipedia_importance_length_correction": -1763.983642578125
},
"fasttext": {
"dclm": 0.9725769758224487,
"english": 0.9103288054466248,
"fineweb_edu_approx": 3.4808669090270996,
"eai_general_math": 0.006086769979447126,
"eai_open_web_math": 0.06287962198257446,
"eai_web_code": 0.021030720323324203
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "551.609713",
"labels": {
"level_1": "Science and Natural history",
"level_2": "Geology and Earth sciences",
"level_3": "Physical geology and Geodynamics"
}
},
"secondary": {
"code": "551.6",
"labels": {
"level_1": "Science and Natural history",
"level_2": "Geology and Earth sciences",
"level_3": "Physical geology and Geodynamics"
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "4",
"label": "Analyze"
},
"secondary": {
"code": "2",
"label": "Understand"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "2",
"label": "Conceptual"
},
"secondary": {
"code": "3",
"label": "Procedural"
}
},
"document_type_v1": {
"primary": {
"code": "3",
"label": "Reference/Encyclopedic/Educational"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "0",
"label": "No Artifacts"
},
"secondary": {
"code": "3",
"label": "Irrelevant Content"
}
},
"missing_content": {
"primary": {
"code": "0",
"label": "No missing content"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"document_type_v2": {
"primary": {
"code": "10",
"label": "Knowledge Article"
},
"secondary": {
"code": "6",
"label": "Content Listing"
}
},
"reasoning_depth": {
"primary": {
"code": "2",
"label": "Basic Reasoning"
},
"secondary": {
"code": "3",
"label": "Intermediate Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "4",
"label": "Highly Correct"
},
"secondary": {
"code": "3",
"label": "Mostly Correct"
}
},
"education_level": {
"primary": {
"code": "1",
"label": "General Audience"
},
"secondary": {
"code": "2",
"label": "High School Level"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
3,463,397,492,504,424,000 |
Have you ever watched or participated in a mosh pit dance? Essentially, every person is moving together while bumping into each other and getting hit. There are plenty of animals that move in groups, such as shoals of fishes, flocks of birds, or swarms of locusts, but most manage to proceed without continually bumping into one another. One of the unique behaviours that has been observed when animals are in groups is when individuals stop before coordinating their movement with that of the animals that surround them. This behaviour is known as intermittent motion. Some scientific work has suggested that the use of intermittent motion is linked to an energy saving response; it has also been associated with a way for animals to gather and analyse information about the environment to guide their movement when in a crowd. Knowing this, Yossef Aidan, Itay Bleichman and Amir Ayali from Tel Aviv University, Israel, decided to find out more about how locusts process visual information from their surroundings and then act on it when moving in a swarm.
The team collected wingless desert locust (Schistocerca gregaria) nymphs – before the final moult when they transform into adults – from a colony at Tel Aviv University. Each locust was tethered so that it could walk on a large rotating ball, essentially a treadmill that allowed the locust to walk in any direction. The ball and insect's movements were tracked using a mouse sensor and a camera located above the insect while the locusts watched a movie of four dots played on monitors in front of them, either moving forward or backward, to simulate the view of nearby insects in a swarm. Aidan and colleagues then created three artificial visual situations for the locusts and monitored how they moved in response to the dots’ movements. In the first experiment, the dots moved continuously, regardless of whether the locust was walking or not. In the second experiment, the dots moved when the locust moved, but stopped when the locust was static. In the third experiment, the dots on the screen moved while the locust was stationary but were motionless when the locust walked on the treadmill.
What the researchers found was very interesting: the locusts moved the most when the moving dots appeared to be going backwards and they moved the least when the dots on the display moved forward continuously. In addition, the team discovered that locusts moved their body to the sides more when the display was moving backwards, and the greatest side movement was observed when the display was moving and the locust was static.
In summary, when a locust is not moving and its surroundings appear to move backwards, it triggers a change in the direction of their movement to stay within the group. This suggests that taking a small break can provide animals with time to process visual information about the movements of neighbouring locusts so they can adjust their own movements accordingly. At the same time, it may also help explain how other animals use the ‘pause-and-move’ response. For example, shoals of fish might use a break to assess the environment and move in time with each other if a predator is approaching. Lastly, the growing field of insect-inspired robotics has produced robots that can fly in formation, but they are not typically programmed to use intermittent motion to help them to remain in sync. Implementation of this behaviour in robots may be beneficial for their performance, so long as they don't get too smart and try to take over the world.
Aidan
,
Y.
,
Bleichman
,
I.
and
Ayali
,
A.
(
2024
).
Pausing to swarm: locust intermittent motion is instrumental for swarming-related visual processing
.
Biol. Lett.
20
,
20230468
.
|
{
"url": "https://journals.biologists.com/jeb/article/227/9/JEB246588/347066/Locusts-take-a-break-to-observe-and-move-with",
"source_domain": "journals.biologists.com",
"snapshot_id": "CC-MAIN-2024-26",
"warc_metadata": {
"Content-Length": "120252",
"Content-Type": "application/http; msgtype=response",
"WARC-Block-Digest": "sha1:6QXJ3YJNYYXSJU5XAIC7NG3ULWS5OQPG",
"WARC-Concurrent-To": "<urn:uuid:f790ca7b-fd5d-440d-bec9-91219286c898>",
"WARC-Date": "2024-06-16T12:55:12Z",
"WARC-IP-Address": "104.18.13.179",
"WARC-Identified-Payload-Type": "text/html",
"WARC-Payload-Digest": "sha1:D7XI2F2NQPMY6C6KR5LZNICBWOYUJSRG",
"WARC-Record-ID": "<urn:uuid:642ad5af-c07b-48de-a9ef-3c6f525f0ce0>",
"WARC-Target-URI": "https://journals.biologists.com/jeb/article/227/9/JEB246588/347066/Locusts-take-a-break-to-observe-and-move-with",
"WARC-Truncated": null,
"WARC-Type": "response",
"WARC-Warcinfo-ID": "<urn:uuid:fa3ed7d2-6a99-4d77-84d8-b7ab8bcea685>"
},
"warc_info": "isPartOf: CC-MAIN-2024-26\r\npublisher: Common Crawl\r\ndescription: Wide crawl of the web for June 2024\r\noperator: Common Crawl Admin (info@commoncrawl.org)\r\nhostname: ip-10-67-67-105\r\nsoftware: Apache Nutch 1.20 (modified, https://github.com/commoncrawl/nutch/)\r\nrobots: checked via crawler-commons 1.5-SNAPSHOT (https://github.com/crawler-commons/crawler-commons)\r\nformat: WARC File Format 1.1\r\nconformsTo: https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/"
}
|
{
"line_start_idx": [
0,
1058,
1059,
2158,
2159,
2588,
2589,
3535,
3536,
3542,
3544,
3547,
3549,
3559,
3561,
3564,
3568,
3574,
3576,
3579,
3581,
3586,
3589,
3689,
3691,
3703,
3706,
3708,
3717
],
"line_end_idx": [
1058,
1059,
2158,
2159,
2588,
2589,
3535,
3536,
3542,
3544,
3547,
3549,
3559,
3561,
3564,
3568,
3574,
3576,
3579,
3581,
3586,
3589,
3689,
3691,
3703,
3706,
3708,
3717,
3718
]
}
|
{
"red_pajama_v2": {
"ccnet_original_length": 3718,
"ccnet_original_nlines": 28,
"rps_doc_curly_bracket": 0,
"rps_doc_ldnoobw_words": 0,
"rps_doc_lorem_ipsum": 0,
"rps_doc_stop_word_fraction": 0.45636624097824097,
"rps_doc_ut1_blacklist": 0,
"rps_doc_frac_all_caps_words": 0.00429185014218092,
"rps_doc_frac_lines_end_with_ellipsis": 0,
"rps_doc_frac_no_alph_words": 0.1158798336982727,
"rps_doc_frac_unique_words": 0.44570502638816833,
"rps_doc_mean_word_length": 4.886548042297363,
"rps_doc_num_sentences": 30,
"rps_doc_symbol_to_word_ratio": 0,
"rps_doc_unigram_entropy": 5.0481390953063965,
"rps_doc_word_count": 617,
"rps_doc_frac_chars_dupe_10grams": 0,
"rps_doc_frac_chars_dupe_5grams": 0.02852405048906803,
"rps_doc_frac_chars_dupe_6grams": 0,
"rps_doc_frac_chars_dupe_7grams": 0,
"rps_doc_frac_chars_dupe_8grams": 0,
"rps_doc_frac_chars_dupe_9grams": 0,
"rps_doc_frac_chars_top_2gram": 0.020895520225167274,
"rps_doc_frac_chars_top_3gram": 0.015920400619506836,
"rps_doc_frac_chars_top_4gram": 0.01459369994699955,
"rps_doc_books_importance": -256.7276916503906,
"rps_doc_books_importance_length_correction": -256.7276916503906,
"rps_doc_openwebtext_importance": -177.67608642578125,
"rps_doc_openwebtext_importance_length_correction": -177.67608642578125,
"rps_doc_wikipedia_importance": -180.8935089111328,
"rps_doc_wikipedia_importance_length_correction": -180.8935089111328
},
"fasttext": {
"dclm": 0.8808730244636536,
"english": 0.9627238512039185,
"fineweb_edu_approx": 3.1812164783477783,
"eai_general_math": 0.32350629568099976,
"eai_open_web_math": 0.27404046058654785,
"eai_web_code": 0.16464293003082275
}
}
|
{
"free_decimal_correspondence": {
"primary": {
"code": "595.78",
"labels": {
"level_1": "Science and Natural history",
"level_2": "Zoology",
"level_3": "Arthropoda and Worms"
}
},
"secondary": {
"code": "591.57",
"labels": {
"level_1": "Science and Natural history",
"level_2": "Zoology",
"level_3": ""
}
}
},
"bloom_cognitive_process": {
"primary": {
"code": "2",
"label": "Understand"
},
"secondary": {
"code": "4",
"label": "Analyze"
}
},
"bloom_knowledge_domain": {
"primary": {
"code": "2",
"label": "Conceptual"
},
"secondary": {
"code": "3",
"label": "Procedural"
}
},
"document_type_v1": {
"primary": {
"code": "3",
"label": "Reference/Encyclopedic/Educational"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"extraction_artifacts": {
"primary": {
"code": "0",
"label": "No Artifacts"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"missing_content": {
"primary": {
"code": "0",
"label": "No missing content"
},
"secondary": {
"code": "-1",
"label": "Abstain"
}
},
"document_type_v2": {
"primary": {
"code": "10",
"label": "Knowledge Article"
},
"secondary": {
"code": "13",
"label": "News (Org.)"
}
},
"reasoning_depth": {
"primary": {
"code": "3",
"label": "Intermediate Reasoning"
},
"secondary": {
"code": "2",
"label": "Basic Reasoning"
}
},
"technical_correctness": {
"primary": {
"code": "3",
"label": "Mostly Correct"
},
"secondary": {
"code": "4",
"label": "Highly Correct"
}
},
"education_level": {
"primary": {
"code": "2",
"label": "High School Level"
},
"secondary": {
"code": "1",
"label": "General Audience"
}
}
}
|
95b707066e5b3184ba9f3885007f98f1
|
6,096,470,680,485,917,000 | "Collectins and ficolins are important in the clearance of endogenous and exogenous danger materials(...TRUNCATED) | {"url":"https://karger.com/jin/article/5/3/242/180268/Collectin-11-MASP-Complex-Formation-Triggers",(...TRUNCATED) | {"line_start_idx":[0,1264,1265,3656,3657,5441,5442,5468,5469,5722,5723,5762,5763,7069,7070,7104,7105(...TRUNCATED) | {"red_pajama_v2":{"ccnet_original_length":36903.0,"ccnet_original_nlines":174.0,"rps_doc_curly_brack(...TRUNCATED) | {"free_decimal_correspondence":{"primary":{"code":"616.0792","labels":{"level_1":"Industrial arts, T(...TRUNCATED) |
95b707066e5b3184ba9f3885007f98f1
|
-6,011,722,529,215,334,000 | "Search query construction issues. Plz help!\n\nI have 2 mysql tables containing data which relate t(...TRUNCATED) | {"url":"https://www.sitepoint.com/community/t/search-query-construction-issues-plz-help/82910","sour(...TRUNCATED) | {"line_start_idx":[0,44,45,111,209,210,327,328,407,567,568,674,675,827,828,966,967,1014,1015,1126,11(...TRUNCATED) | {"red_pajama_v2":{"ccnet_original_length":6869.0,"ccnet_original_nlines":137.0,"rps_doc_curly_bracke(...TRUNCATED) | {"free_decimal_correspondence":{"primary":{"code":"005.74","labels":{"level_1":"General works, books(...TRUNCATED) |
95b707066e5b3184ba9f3885007f98f1
|
7,644,481,838,083,769,000 | "Exploring the Benefits of Lidocaine Cream Over the Counter\n\nAre you curious about the benefits of(...TRUNCATED) | {"url":"https://www.kartal24.com/en/exploring-the-benefits-of-lidocaine-cream-over-the-counter","sou(...TRUNCATED) | {"line_start_idx":[0,59,60,494,495,526,527,1374,1375,1413,1414,2129,2130,2185,2186,3118,3119,3182,31(...TRUNCATED) | {"red_pajama_v2":{"ccnet_original_length":5485.0,"ccnet_original_nlines":26.0,"rps_doc_curly_bracket(...TRUNCATED) | {"free_decimal_correspondence":{"primary":{"code":"615.6","labels":{"level_1":"Industrial arts, Tech(...TRUNCATED) |
95b707066e5b3184ba9f3885007f98f1
|
End of preview.
🔬 EAI-Taxonomy STEM w/ DCLM
A high-quality STEM dataset curated from web data using taxonomy-based filtering, containing 1742 billion tokens of science, technology, engineering, and mathematics content.
🎯 Dataset Overview
This dataset is part of the Essential-Web project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional STEM datasets that require complex domain-specific pipelines, our approach leverages a 12-category taxonomy to efficiently identify and extract high-quality STEM content.
🧪 EAI-Taxonomy STEM w/ DCLM (1742B tokens): Documents targeting science, engineering, medical, and computer science content that exhibit reasoning, combined with the DCLM classifier to filter for instruction-dense documents.
🏆 Performance
Our taxonomy-based approach achieves superior results with significantly less curation effort:
| Dataset | MMLU-STEM | Curation Complexity |
|---|---|---|
| DCLM-baseline | 27.7% | General web filtering |
| FineWeb-Edu | 26.7% | Educational filtering |
| EAI-Taxonomy STEM | 29.1% | Simple semantic filter |
| EAI-Taxonomy STEM w/ DCLM | 34.5% | + DCLM classifier |
Results show +24.5% improvement over DCLM and +29.2% improvement over FineWeb-Edu.
🔍 Key Findings
- Strong STEM Performance: Outperforms baseline and educational datasets beyond standard error
- Efficient Curation: Achieves superior results without complex domain-specific pipelines
- Broad Coverage: Encompasses science, engineering, medical, and computer science domains
- Quality Focus: Selects high-quality document types and filters for reasoning content
Dataset Schema Documentation
Overview
This dataset contains web-crawled text data with comprehensive metadata, quality signals, and taxonomic classifications. Each record represents a document extracted from web archives with detailed provenance tracking and quality assessment metrics.
Core Fields
| Field | Type | Description | Path |
|---|---|---|---|
id |
Int64 |
Unique identifier based on document hash | id |
text |
String |
The main textual content of the document | text |
EAI Taxonomy Classification
Comprehensive hierarchical classification system with primary and secondary labels - the most important feature of this dataset. The taxonomy is designed to provide detailed subject categorization, document type identification, content quality assessment, and extraction quality indicators.
Free Decimal Correspondence (FDC)
A Dewey Decimal-inspired classification system with 3-level hierarchical labels. The FDC provides nested categories where each successive level refines its parent category. It's designed to be compatible with the Dewey Decimal System for library cataloging.
Level Structure:
- Level 1: Top-level categories (0-9) covering broad subject areas like General works, Philosophy, Religion, Social Sciences, etc.
- Level 2: Sub-divisions (00-99) that refine Level 1 categories
- Level 3: Specific categories (000-999) that further refine Level 2 categories
| Component | Description | Path |
|---|---|---|
| Primary Code | Main classification code | eai_taxonomy.free_decimal_correspondence.primary.code |
| Primary Level 1 | Top-level category (0=General works, 1=Philosophy, 2=Religion, 3=Social Sciences, 4=Language, 5=Science, 6=Technology, 7=Arts, 8=Literature, 9=History/Geography) | eai_taxonomy.free_decimal_correspondence.primary.labels.level_1 |
| Primary Level 2 | Mid-level category | eai_taxonomy.free_decimal_correspondence.primary.labels.level_2 |
| Primary Level 3 | Specific category | eai_taxonomy.free_decimal_correspondence.primary.labels.level_3 |
| Secondary Code | Alternative classification code | eai_taxonomy.free_decimal_correspondence.secondary.code |
| Secondary Level 1 | Alternative top-level category | eai_taxonomy.free_decimal_correspondence.secondary.labels.level_1 |
| Secondary Level 2 | Alternative mid-level category | eai_taxonomy.free_decimal_correspondence.secondary.labels.level_2 |
| Secondary Level 3 | Alternative specific category | eai_taxonomy.free_decimal_correspondence.secondary.labels.level_3 |
We recommend this viewer for easily navigating the FDC categories when curating filters: https://www.librarything.com/mds
Bloom's Taxonomy Integration
Based on Anderson and Krathwohl's 2001 revision of Bloom's Taxonomy of Educational Objectives, providing two complementary categorization dimensions for educational content analysis.
Knowledge Domain
Categorizes the type of knowledge demonstrated in the document:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main knowledge domain code | eai_taxonomy.bloom_knowledge_domain.primary.code |
| Primary Label | Main knowledge domain label | eai_taxonomy.bloom_knowledge_domain.primary.label |
| Secondary Code | Alternative knowledge domain code | eai_taxonomy.bloom_knowledge_domain.secondary.code |
| Secondary Label | Alternative knowledge domain label | eai_taxonomy.bloom_knowledge_domain.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
1 |
Factual | Basic elements to learn or solve problems |
2 |
Conceptual | Interrelationships between basic elements within larger context |
3 |
Procedural | Methods and techniques in the discipline |
4 |
Metacognitive | Awareness of how learning works in relation to oneself |
Cognitive Processing Level
Assesses the learning and thinking skill levels demonstrated by the document author:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main cognitive process code | eai_taxonomy.bloom_cognitive_process.primary.code |
| Primary Label | Main cognitive process label | eai_taxonomy.bloom_cognitive_process.primary.label |
| Secondary Code | Alternative cognitive process code | eai_taxonomy.bloom_cognitive_process.secondary.code |
| Secondary Label | Alternative cognitive process label | eai_taxonomy.bloom_cognitive_process.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
1 |
Remember | Retrieve relevant knowledge from memory |
2 |
Understand | Determine meaning of instructional messages |
3 |
Apply | Use a procedure in a given situation |
4 |
Analyze | Break materials into components and determine relationships |
5 |
Evaluate | Make judgments based on criteria and standards |
6 |
Create | Create new or original work |
Document Characteristics
Document Type v1
In-house classification of common web document types and formats:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main document type code | eai_taxonomy.document_type_v1.primary.code |
| Primary Label | Main document type label | eai_taxonomy.document_type_v1.primary.label |
| Secondary Code | Alternative document type code | eai_taxonomy.document_type_v1.secondary.code |
| Secondary Label | Alternative document type label | eai_taxonomy.document_type_v1.secondary.label |
Possible Values:
| Code | Label | Examples |
|---|---|---|
-1 |
Abstain | Unable to classify |
1 |
News/Editorial | CNN articles, opinion columns |
2 |
Academic/Research | ArXiv papers, research articles |
3 |
Reference/Encyclopedic/Educational | FAQs, Wikipedia entries |
4 |
Code/Software | GitHub repos, code examples |
5 |
Social/Forum | Conversation threads, Q&A boards |
6 |
Promotional/Advertisement | Product pages, calls to action |
7 |
Search/Directory/Bibliography | Link pages, search results |
8 |
Adult/Pornographic | Adult content |
9 |
Personal/Misc | Blogs, user profiles |
10 |
Machine-Generated | Lorem ipsum, garbled text |
11 |
Legal/Regulatory | Contracts, terms of service |
12 |
Government/Political | Legislation, press releases |
13 |
Literary/Creative | Poems, short stories |
14 |
Reviews/Critiques | Film critiques, product reviews |
15 |
E-Commerce/Marketplace | eBay listings, Amazon pages |
16 |
Images/Videos/Audio | YouTube videos, Imgur pages |
17 |
Other/Unclassified | Documents that resist classification |
Document Type v2
Updated classification based on WebOrganizer taxonomy with refined categories for improved document classification accuracy:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main document type code (v2) | eai_taxonomy.document_type_v2.primary.code |
| Primary Label | Main document type label (v2) | eai_taxonomy.document_type_v2.primary.label |
| Secondary Code | Alternative document type code (v2) | eai_taxonomy.document_type_v2.secondary.code |
| Secondary Label | Alternative document type label (v2) | eai_taxonomy.document_type_v2.secondary.label |
Complete Value Mapping:
| Code | Label | Examples |
|---|---|---|
-1 |
Abstain | Documents requiring human review |
1 |
About (Org.) | Company about pages, mission statements |
2 |
About (Personal) | Personal bios, LinkedIn profiles |
3 |
Academic Writing | Research papers, abstracts, dissertations |
4 |
Audio Transcript | Interview transcripts, court records, captions |
5 |
Comment Section | Reddit threads, blog comments |
6 |
Content Listing | Site maps, product catalogs, directory listings |
7 |
Creative Writing | Song lyrics, novel excerpts, poetry |
8 |
Documentation | API docs, README files, user manuals |
9 |
FAQ | FAQ pages, Q&A lists |
10 |
Knowledge Article | Wikipedia articles, Britannica entries |
11 |
Legal Notices | Privacy policies, license agreements, terms of service |
12 |
Listicle | Buzzfeed-style articles, "Top 10" lists |
13 |
News (Org.) | Government blog posts, corporate announcements |
14 |
News Article | Newspaper articles, CNN content, breaking news |
15 |
Nonfiction Writing | Editorials, obituaries, memoirs, opinion pieces |
16 |
Personal Blog | Personal journals, diary entries, lifestyle blogs |
17 |
Product Page | Product descriptions, course offerings, sales pages |
18 |
Q&A Forum | Quora posts, Stack Exchange discussions |
19 |
Spam / Ads | SEO keyword stuffing, promotional spam |
20 |
Structured Data | Datasheets, glossaries, JSON files, databases |
21 |
Customer Support | Help articles, troubleshooting guides |
22 |
Truncated | Paywalled sites, image galleries, partial content |
23 |
Tutorial | Cooking recipes, WikiHow pages, step-by-step guides |
24 |
User Review | Yelp reviews, TripAdvisor feedback, product reviews |
25 |
Other/Unclassified | Miscellaneous documents not fitting other categories |
Extraction Artifacts
Assessment of technical extraction quality, identifying issues from HTML-to-text conversion:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main extraction artifact code | eai_taxonomy.extraction_artifacts.primary.code |
| Primary Label | Main extraction artifact label | eai_taxonomy.extraction_artifacts.primary.label |
| Secondary Code | Alternative extraction artifact code | eai_taxonomy.extraction_artifacts.secondary.code |
| Secondary Label | Alternative extraction artifact label | eai_taxonomy.extraction_artifacts.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
0 |
No Artifacts | Clean text with no leftover HTML or irrelevant elements |
1 |
Leftover HTML | HTML/code artifacts remaining after extraction |
2 |
Text Extraction Errors | Broken math expressions, encoding errors, improperly parsed tables |
3 |
Irrelevant Content | Headers, footers, nav menus extracted by mistake |
4 |
Indeterminate | Insufficient content to judge |
Missing Content
Assessment of content completeness and extraction success:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main missing content code | eai_taxonomy.missing_content.primary.code |
| Primary Label | Main missing content label | eai_taxonomy.missing_content.primary.label |
| Secondary Code | Alternative missing content code | eai_taxonomy.missing_content.secondary.code |
| Secondary Label | Alternative missing content label | eai_taxonomy.missing_content.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
0 |
No Missing Content | Complete and coherent text |
1 |
Truncated Snippets | Obvious "...", incomplete paragraphs, cut-off text |
2 |
Click Here References | "Download here", "Click here" without linked content |
3 |
Incoherent Flow | Unreadable or illogical flow due to missing context |
4 |
Missing Images or Figures | Placeholders or references to missing visual content |
5 |
Missing Referenced Data | References to absent tables/datasets (e.g., "See Table 3") |
6 |
Indeterminate | Insufficient content to judge |
Text Structure Information
| Field | Type | Description | Path |
|---|---|---|---|
| Line Start Indices | List[Int32] |
Starting indices of each line | line_start_n_end_idx.line_start_idx |
| Line End Indices | List[Int32] |
Ending indices of each line | line_start_n_end_idx.line_end_idx |
Content Quality Dimensions
Quality assessment inspired by NaturalReasoning and FineWeb efforts to categorize web data by information sophistication.
Reasoning Depth
Assesses the complexity and sophistication of logical reasoning in the document:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main reasoning depth code | eai_taxonomy.reasoning_depth.primary.code |
| Primary Label | Main reasoning depth label | eai_taxonomy.reasoning_depth.primary.label |
| Secondary Code | Alternative reasoning depth code | eai_taxonomy.reasoning_depth.secondary.code |
| Secondary Label | Alternative reasoning depth label | eai_taxonomy.reasoning_depth.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
1 |
No Reasoning | Facts present but no evidence of reasoning |
2 |
Basic Reasoning | Basic analysis with minimal explanation and summarization |
3 |
Intermediate Reasoning | Some logical steps connecting ideas and structured thinking |
4 |
Advanced Reasoning | Multi-step reasoning and thorough analysis with well-developed explanations |
5 |
Exceptional Reasoning | Novel abstractions, theoretical frameworks, long chain-of-thought, original insights, or proofs |
6 |
Indeterminate | Insufficient context to judge |
Technical Correctness
Evaluates the accuracy and precision of technical information:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main technical correctness code | eai_taxonomy.technical_correctness.primary.code |
| Primary Label | Main technical correctness label | eai_taxonomy.technical_correctness.primary.label |
| Secondary Code | Alternative technical correctness code | eai_taxonomy.technical_correctness.secondary.code |
| Secondary Label | Alternative technical correctness label | eai_taxonomy.technical_correctness.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
1 |
Technically Flawed | Significant errors undermining content validity |
2 |
Partially Correct | Some correctness but contains flaws, omissions, or errors |
3 |
Mostly Correct | Technical correctness with minor flaws or incomplete explanations |
4 |
Highly Correct | High technical correctness with precise definitions and clear explanations |
5 |
Exceptionally Correct | Exceptional technical correctness with formal proofs and flawless content |
6 |
Not Applicable/Indeterminate | No technical content or insufficient context |
Education Level
Assesses the appropriate educational background required to comprehend the content:
| Component | Description | Path |
|---|---|---|
| Primary Code | Main education level code | eai_taxonomy.education_level.primary.code |
| Primary Label | Main education level label | eai_taxonomy.education_level.primary.label |
| Secondary Code | Alternative education level code | eai_taxonomy.education_level.secondary.code |
| Secondary Label | Alternative education level label | eai_taxonomy.education_level.secondary.label |
Possible Values:
| Code | Label | Description |
|---|---|---|
-1 |
Abstain | Unable to determine |
1 |
General Audience | Accessible to anyone with basic literacy; simple terms |
2 |
High School Level | Requires high school education; specialized terminology explained for non-experts |
3 |
Undergraduate Level | Requires college education; uses specialized terminology and assumes background knowledge |
4 |
Graduate/Expert Level | Requires graduate education or domain expertise; assumes deep background knowledge |
5 |
Indeterminate | Insufficient content to judge educational level |
Metadata
Metadata Structure
The metadata field contains a nested structure with web archive information:
| Field | Type | Description | Path |
|---|---|---|---|
| URL Information | |||
| URL | String |
Original URL of the document | metadata.url |
| Source Domain | String |
Domain name of the source | metadata.source_domain |
| Snapshot ID | String |
Identifier for the web archive snapshot | metadata.snapshot_id |
| WARC Metadata | WARC (Web ARChive) format metadata | ||
| Content Length | String |
Size of the content | metadata.warc_metadata.Content-Length |
| Content Type | String |
MIME type of the content | metadata.warc_metadata.Content-Type |
| Block Digest | String |
Checksum of the WARC block | metadata.warc_metadata.WARC-Block-Digest |
| Concurrent To | String |
Related WARC records | metadata.warc_metadata.WARC-Concurrent-To |
| Date | String |
Timestamp of the crawl | metadata.warc_metadata.WARC-Date |
| IP Address | String |
Source server IP address | metadata.warc_metadata.WARC-IP-Address |
| Payload Type | String |
Identified content type | metadata.warc_metadata.WARC-Identified-Payload-Type |
| Payload Digest | String |
Checksum of the payload | metadata.warc_metadata.WARC-Payload-Digest |
| Record ID | String |
Unique WARC record identifier | metadata.warc_metadata.WARC-Record-ID |
| Target URI | String |
Original target URL | metadata.warc_metadata.WARC-Target-URI |
| Truncated | String |
Truncation status | metadata.warc_metadata.WARC-Truncated |
| Type | String |
WARC record type | metadata.warc_metadata.WARC-Type |
| Warcinfo ID | String |
Associated warcinfo record | metadata.warc_metadata.WARC-Warcinfo-ID |
| Additional Info | |||
| WARC Info | String |
Additional WARC information | metadata.warc_info |
Quality Signals
The dataset includes two comprehensive quality assessment frameworks:
Red Pajama v2 Quality Metrics
Text quality indicators derived from the Red Pajama v2 filtering pipeline:
Content Structure Metrics
| Metric | Description | Path |
|---|---|---|
| Original Length | Original document length | quality_signals.red_pajama_v2.ccnet_original_length |
| Original Lines | Number of lines in original document | quality_signals.red_pajama_v2.ccnet_original_nlines |
| Sentence Count | Total sentence count | quality_signals.red_pajama_v2.rps_doc_num_sentences |
| Word Count | Total word count | quality_signals.red_pajama_v2.rps_doc_word_count |
| Mean Word Length | Average word length | quality_signals.red_pajama_v2.rps_doc_mean_word_length |
Language Quality Metrics
| Metric | Description | Path |
|---|---|---|
| Stop Word Fraction | Proportion of stop words | quality_signals.red_pajama_v2.rps_doc_stop_word_fraction |
| Unique Words Fraction | Fraction of unique words | quality_signals.red_pajama_v2.rps_doc_frac_unique_words |
| All Caps Words | Fraction of words in all capitals | quality_signals.red_pajama_v2.rps_doc_frac_all_caps_words |
| Non-Alphabetic Words | Fraction of non-alphabetic words | quality_signals.red_pajama_v2.rps_doc_frac_no_alph_words |
| Unigram Entropy | Entropy measure of word distribution | quality_signals.red_pajama_v2.rps_doc_unigram_entropy |
Content Pattern Analysis
| Metric | Description | Path |
|---|---|---|
| Curly Bracket Density | Curly bracket density (code indicator) | quality_signals.red_pajama_v2.rps_doc_curly_bracket |
| Symbol-to-Word Ratio | Symbol-to-word ratio | quality_signals.red_pajama_v2.rps_doc_symbol_to_word_ratio |
| Ellipsis Line Endings | Lines ending with ellipsis | quality_signals.red_pajama_v2.rps_doc_frac_lines_end_with_ellipsis |
| Lorem Ipsum Detection | Lorem ipsum text detection | quality_signals.red_pajama_v2.rps_doc_lorem_ipsum |
| Offensive Content | Potentially offensive content detection | quality_signals.red_pajama_v2.rps_doc_ldnoobw_words |
| UT1 Blacklist | UT1 blacklist filtering score | quality_signals.red_pajama_v2.rps_doc_ut1_blacklist |
Duplication Detection
| Metric | Description | Path |
|---|---|---|
| 5-gram Duplication | Character-level duplication for 5-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_5grams |
| 6-gram Duplication | Character-level duplication for 6-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_6grams |
| 7-gram Duplication | Character-level duplication for 7-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_7grams |
| 8-gram Duplication | Character-level duplication for 8-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_8grams |
| 9-gram Duplication | Character-level duplication for 9-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_9grams |
| 10-gram Duplication | Character-level duplication for 10-grams | quality_signals.red_pajama_v2.rps_doc_frac_chars_dupe_10grams |
| Top 2-gram Coverage | Most frequent 2-gram coverage | quality_signals.red_pajama_v2.rps_doc_frac_chars_top_2gram |
| Top 3-gram Coverage | Most frequent 3-gram coverage | quality_signals.red_pajama_v2.rps_doc_frac_chars_top_3gram |
| Top 4-gram Coverage | Most frequent 4-gram coverage | quality_signals.red_pajama_v2.rps_doc_frac_chars_top_4gram |
Domain Importance Scores
| Metric | Description | Path |
|---|---|---|
| Books Importance | Similarity to book content | quality_signals.red_pajama_v2.rps_doc_books_importance |
| Books Importance (Length Corrected) | Length-corrected books similarity | quality_signals.red_pajama_v2.rps_doc_books_importance_length_correction |
| OpenWebText Importance | Similarity to OpenWebText | quality_signals.red_pajama_v2.rps_doc_openwebtext_importance |
| OpenWebText Importance (Length Corrected) | Length-corrected OpenWebText similarity | quality_signals.red_pajama_v2.rps_doc_openwebtext_importance_length_correction |
| Wikipedia Importance | Similarity to Wikipedia | quality_signals.red_pajama_v2.rps_doc_wikipedia_importance |
| Wikipedia Importance (Length Corrected) | Length-corrected Wikipedia similarity | quality_signals.red_pajama_v2.rps_doc_wikipedia_importance_length_correction |
FastText Classification Scores
Domain and content type classification probabilities:
| Metric | Description | Path |
|---|---|---|
| DCLM Score | DataComp-LM classifier score | quality_signals.fasttext.dclm |
| English Confidence | English language confidence | quality_signals.fasttext.english |
| Educational Content | Educational content approximation | quality_signals.fasttext.fineweb_edu_approx |
| General Math | General mathematics content | quality_signals.fasttext.eai_general_math |
| Web Math | OWM Web-based mathematics content | quality_signals.fasttext.eai_open_web_math |
| Code Content | Code content detection | quality_signals.fasttext.eai_web_code |
How to Load the Dataset
This section provides examples of how to load the EssentialAI/eai-taxonomy-stem-w-dclm dataset using different Python libraries and frameworks.
Using Hugging Face Datasets (Standard Method)
The simplest way to load the dataset is using the Hugging Face datasets library:
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm")
# View dataset structure
print(dataset)
print(f"Number of examples: {len(dataset['train'])}")
You can also load the dataset in streaming mode to avoid downloading the entire dataset at once:
from datasets import load_dataset
# Load in streaming mode
dataset = load_dataset("EssentialAI/eai-taxonomy-stem-w-dclm", streaming=True)
data_stream = dataset["train"]
# Iterate through examples
for example in data_stream.take(5):
print(example)
Using PySpark
For large-scale distributed processing, you can load the dataset using PySpark with the pyspark_huggingface library:
# First install the required library:
# pip install pyspark_huggingface
import pyspark_huggingface
from pyspark.sql import SparkSession
# Initialize Spark session
spark = SparkSession.builder.appName("EAI-Taxonomy-STEM-w-DCLM").getOrCreate()
# Load the dataset using the "huggingface" data source
df = spark.read.format("huggingface").load("EssentialAI/eai-taxonomy-stem-w-dclm")
# Basic dataset exploration
print(f"Dataset shape: {df.count()} rows, {len(df.columns)} columns")
df.show(10)
df.printSchema()
# Load only specific columns for efficiency
df_subset = (
spark.read.format("huggingface")
.option("columns", '["column1", "column2"]') # Replace with actual column names
.load("EssentialAI/eai-taxonomy-stem-w-dclm")
)
# Run SQL queries on the dataset
df.createOrReplaceTempView("eai_taxonomy_stem_w_dclm_dataset")
result = spark.sql("""
SELECT COUNT(*) as total_examples
FROM eai_taxonomy_stem_w_dclm_dataset
""")
result.show()
Using Daft
Daft provides a modern DataFrame library optimized for machine learning workloads. You can load the dataset directly from Hugging Face:
import daft
# Load the entire dataset
df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm")
# Basic exploration
print("Dataset schema:")
df.schema()
print("First 5 rows:")
df.show(5)
If you need to access private datasets or use authentication:
import daft
from daft.io import IOConfig, HTTPConfig
io_config = IOConfig(http=HTTPConfig(bearer_token="your_token"))
df = daft.read_parquet("hf://datasets/EssentialAI/eai-taxonomy-stem-w-dclm", io_config=io_config)
Installation Requirements
Make sure you have the required libraries installed:
# For Hugging Face datasets
pip install datasets
# For PySpark with Hugging Face integration
pip install pyspark_huggingface
# For Daft
pip install daft
📜 License
Essential-Web-v1.0 contributions are made available under the ODC attribution license; however, users should also abide by the Common Crawl - Terms of Use. We do not alter the license of any of the underlying data.
📝 Citation
@misc{ai2025essentialwebv1024ttokens,
title={Essential-Web v1.0: 24T tokens of organized web data},
author={Essential AI and : and Andrew Hojel and Michael Pust and Tim Romanski and Yash Vanjani and Ritvik Kapila and Mohit Parmar and Adarsh Chaluvaraju and Alok Tripathy and Anil Thomas and Ashish Tanwer and Darsh J Shah and Ishaan Shah and Karl Stratos and Khoi Nguyen and Kurt Smith and Michael Callahan and Peter Rushton and Philip Monk and Platon Mazarakis and Saad Jamal and Saurabh Srivastava and Somanshu Singla and Ashish Vaswani},
year={2025},
eprint={2506.14111},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.14111},
}
- Downloads last month
- 9,537