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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 2 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
Learning recursive functions: A survey
Thomas Zeugmann, Sandra Zilles
2008· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
Bias learning, knowledge sharing
Joumana Ghosn, Yoshua Bengio
2003· article· en· IEEE Transactions on Neural Networks· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
On Nesting Monte Carlo Estimators
Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood
2017· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affno abstractunlabeled
The complexity of properly learning simple concept classes
Misha Alekhnovich, Mark Braverman, Vitaly Feldman, Adam R. Klivans, Toniann Pitassi
2007· article· en· Journal of Computer and System Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affno abstractunlabeled
Foundations of Non-Bayesian Social Learning
Pooya Molavi, Alireza Tahbaz-Salehi, Ali Jadbabaie
2015· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
Feature value acquisition in testing
Victor S. Sheng, Charles X. Ling
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
Clustering with Same-Cluster Queries
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
2016· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Active learning in heteroscedastic noise
András Antos, Varun Grover, Csaba Szepesvári
2010· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations
affno abstractunlabeled
Cybernetics and Learning Automata
B. John Oommen, Sudip Misra
2009· book-chapter· en· Springer handbooks· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations
afffundunlabeled
A System for Efficient High-Recall Retrieval
Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
venueno affno abstractunlabeled
Bootstrapped Self Training for Knowledge Base Population.
Gabor Angeli, Victor W. Zhong, Danqi Chen, Arun Tejasvi Chaganty, Jason Bolton, Melvin Jose Johnson Premkumar +3 more
2015· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
PLAL: Cluster-based active learning
Ruth Urner, Sharon Wulff, Shai Ben-David
2013· article· en· Conference on Learning Theory· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Learning from Weak Teachers
Ruth Urner, Shai Ben-David, Ohad Shamir
2012· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
Agnostic Bayesian Learning of Ensembles
Alexandre Lacoste, Mario Marchand, Fran ois Laviolette, Hugo Larochelle
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
{PAC-Bayesian Theory for Transductive Learning}
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy
2014· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
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