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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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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
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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.

587 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.
587 works in the cohort · of 4,299,418page 12 of 12

Labels cover 1 of 587 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 587 of 587 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.

afffundunlabeled
Alternative Forms of Bounded Suboptimal Search
Richard Valenzano, Shahab Jabbari Arfaee, Jordan Thayer, Roni Stern
2021· article· en· Proceedings of the International Symposium on Combinatorial Search· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Welfare-Maximizing Pooled Testing
Simon Finster, Michelle González Amador, Edwin Lock, Francisco J. Marmolejo-Cossío, Evi Micha, Ariel Procaccia
2024· article· en· ACM SIGecom Exchanges· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
NOVEL MACHINE LEARNING ALGORITHMS
Alireza Farhangfar
2013· article· en· University of Alberta Library· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The query complexity of order-finding
Richard Cleve
2004· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Identifying Regions of Trusted Predictions
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
2021· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Noisy Computing of the OR and MAX Functions
Banghua Zhu, Ziao Wang, Nadim Ghaddar, Jiantao Jiao, Lele Wang
2024· article· en· IEEE Journal on Selected Areas in Information Theory· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Low Degree Testing over the Reals
Vipul Arora, Arnab Bhattacharyya, Noah Fleming, Esty Kelman, Yuichi Yoshida
2022· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Foundations of Bayesian Learning
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Comparison of Tools and Methods for Technology-Assisted Review
Tom O’Halloran, Bronagh McManus, Andrew Harbison, Maura R. Grossman, Gordon V. Cormack
2024· book-chapter· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About