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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
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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 5 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.

affno abstractunlabeled
Active Learning with c-Certainty
Eileen A. Ni, Charles X. Ling
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
fundno affunlabeled
Private and Online Learnability Are Equivalent
Noga Alon, Mark Bun, Roi Livni, M. Malliaris, Shay Moran
2022· article· en· Journal of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Some natural conditions on incremental learning
Sanjay Jain, Steffen Lange, Sandra Zilles
2007· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Sample-Efficient Learning of Mixtures
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
State Complexity Research and Approximation
Sheng Yü, Yuan Gao
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Learning without coding
Sanjay Jain, Samuel E. Moelius, Sandra Zilles
2012· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
aboutno affunlabeled
Three papers on boosting: an introduction
Vladimir Koltchinskii, Bin Yu
2004· article· en· The Annals of Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Multitask Spectral Learning of Weighted Automata
Guillaume Rabusseau, Borja Balle, Joëlle Pineau
2017· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
XOR Codes and Sparse Learning Parity with Noise
Andrej Bogdanov, Manuel Sabin, Prashant Nalini Vasudevan
2019· book-chapter· en· Society for Industrial and Applied Mathematics eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Learning and Testing Junta Distributions
Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld
2016· article· en· Conference on Learning Theory· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
A PAC-Bayes approach to the Set Covering Machine
François Laviolette, Mario Marchand, Mohak Shah
2005· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Survey Propagation beyond Constraint Satisfaction Problems.
Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey
2016· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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