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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Numerical Methods and Algorithms
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

452 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
452 works in the cohort · of 4,299,418page 7 of 10

Labels cover 0 of 452 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 452 of 452 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Computing Clipped Products
Arthur C. Norman, Stephen M. Watt
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Fast number parsing without fallback
Noble Mushtak, Daniel Lemire
2023· article· en· Software Practice and Experience· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
Fast and accurate computation for kernel estimators
Qingguo Tang, Rohana J. Karunamuni
2015· article· en· Computational Statistics & Data Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Scientific Computing by Numerical Methods
Christina C. Christara, Kenneth R. Jackson
2003· other· en· digital Encyclopedia of Applied Physics· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Root-Finding with Implicit Deflation
Rémi Imbach, Victor Y. Pan, Chee Yap, Ilias Kotsireas, Vitaly Zaderman
2019· preprint· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
2017 Kleene award
Prakash Panangaden
2017· article· en· ACM SIGLOG News· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
How to hunt wild constants
David R. Stoutemyer
2023· preprint· en· Maple Transactions· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Coding Examples
2024· other· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affno abstractunlabeled
Closest Approximations to Real Numbers.
Amitabha Tripathi, Sujith Vijay
2005· article· en· Ars Combinatoria· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
HOUR(s and Hours) OF (Math +) CODE
George Gadanidis, Rosa Cendros Araujo
2018· article· ru· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Numbers
Alex Gezerlis
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About