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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.

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 3 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
Active Learning for One-Class Classification
Vincent Barnabé-Lortie, Colin Bellinger, Nathalie Japkowicz
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
The Decision List Machine
Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe‐Taylor
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Maximizing agreements and coagnostic learning
Nader H. Bshouty, Lynn Burroughs
2005· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Dual search in permutation state spaces
Uzi Zahavi, Ariel Feiner, Robert C. Holte, Jonathan Schaeffer
2006· article· en· National Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Bootstrapping via Graph Propagation
Max Whitney, Anoop Sarkar
2012· article· en· Summit (Simon Fraser University)· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
MechanicalHeart
William Callaghan, Joslin Goh, Michael M. Mohareb, Andrew Lim, Edith Law
2018· article· en· Proceedings of the ACM on Human-Computer Interaction· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
fundno affunlabeled
Back to the Future:Knowledge Light Case Base Cookery
Qian Zhang, Rong Hu, Brian Mac Namee, Sarah Jane Delany
2021· article· en· Arrow@dit (Dublin Institute of Technology)· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Beyond EDSM
Orlando Cicchello, Stefan C. Kremer
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Multiple-Choice Randomization
Ian K. McLeod, Ying Zhang, Yu Hao
2003· article· en· Journal of Statistics Education· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
State Complexity Approximation
Yuan Gao, Sheng Yü
2009· article· en· Electronic Proceedings in Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Deep Inverse Optimization
Yingcong Tan, Andrew Delong, Daria Terekhov
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Approximation algorithms for NMR spectral peak assignment
Zhi‐Zhong Chen, Tao Jiang, Guohui Lin, Jianjun Wen, Dong Xu, Jinbo Xu +1 more
2003· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Online Learning with Costly Features and Labels
Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
afffundno abstractunlabeled
Mind change efficient learning
Wei Luo, Oliver Schulte
2006· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Preference-based teaching
Ziyuan Gao, Christoph Ries, Hans Ulrich Simon, Sandra Zilles
2017· article· en· Journal of Machine Learning Research· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
afffundunlabeled
GP under streaming data constraints
Aaron Atwater, Malcolm I. Heywood, A. Nur Zincir‐Heywood
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
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