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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.

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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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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.

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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.

3,084 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
3,084 works in the cohort · of 4,299,418page 54 of 62

Labels cover 10 of 3,084 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 3,084 of 3,084 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.

fundno affunlabeled
LLMJudge: LLMs for Relevance Judgments
Hossein A. Rahmani, Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Paul Thomas, Charles L. A. Clarke +3 more
2024· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Working with Symbol Warping Tools
Jennifer Harder
2022· book-chapter· en· Apress eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Gumbel Counterfactual Generation From Language Models
Shauli Ravfogel, Anej Svete, Vésteinn Snæbjarnarson, Ryan Cotterell
2024· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
TLMD: Tigrinya Language Modeling Dataset
Fitsum Gaim, Wonsuk Yang, Jong Cheol Park
2021· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Translation Machinery
Muriel Gargaud, William M. Irvine, Ricardo Amils, Daniele L. Pinti, José Cernicharo Quintanilla, Daniel Rouan +3 more
2015· book-chapter· en· Encyclopedia of Astrobiology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
AIHEC American Indian Collections Portal
Carrie Lynn Billy
2012· article· en· Humanities Commons CORE (Modern Language Association / Columbia University)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
fundno affunlabeled
Scaling Trends in Language Model Robustness
Nikolaus H. R. Howe, Ian R. McKenzie, Oskar J. Hollinsworth, Michał Zając, Tom Tseng, Aaron Tucker +2 more
2024· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
SVG contre Flash : théorie et pratique
M. Beauchemin
2015· article· fr· Documentation et bibliothèques· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Translation
Linda Bird
2025· book-chapter· en· Health information technology standards· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Parsing with Context-Free Grammars
2024· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
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