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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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Machine Learning and Algorithms
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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 1 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.

affunlabeled
Deep Bayesian Active Learning with Image Data
2017· article· en· Oxford University Research Archive (ORA) (University of Oxford)· Computer Science
machine prediction:candidate · noneconsensus · none
435
citations
afffundno abstractunlabeled
Model complexity of deep learning: a survey
Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian
2021· article· en· Knowledge and Information Systems· Computer Science
machine prediction:candidate · noneconsensus · none
388
citations
affunlabeled
Zero-data learning of new tasks
Hugo Larochelle, Dumitru Erhan, Yoshua Bengio
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
353
citations
affunlabeled
XTRACT
Minos Garofalakis, Aristides Gionis, Rajeev Rastogi, S. Seshadri, Kyuseok Shim
2000· article· en· ACM SIGMOD Record· Computer Science
machine prediction:candidate · noneconsensus · none
202
citations
afffundunlabeled
PAC-Bayesian learning of linear classifiers
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
177
citations
affno abstractunlabeled
On learning and branching: a survey
Andrea Lodi, Giulia Zarpellon
2017· article· en· Top· Computer Science
machine prediction:candidate · noneconsensus · none
174
citations
affno abstractunlabeled
A Continuous Max-Flow Approach to Potts Model
Jing Yuan, Egil Bae, Xue‐Cheng Tai, Yuri Boykov
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
163
citations
affunlabeled
Artificial Intelligence
David Poole, Alan K. Mackworth
2017· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
152
citations
affunlabeled
Active Preference Learning with Discrete Choice Data
Eric Brochu, Nando de Freitas, Abhijeet Ghosh
2007· article· en· Oxford University Research Archive (ORA) (University of Oxford)· Computer Science
machine prediction:candidate · noneconsensus · none
131
citations
affunlabeled
Learning functions represented as multiplicity automata
Amos Beimel, Francesco Bergadano, Nader H. Bshouty, Eyal Kushilevitz, Stefano Varricchio
2000· article· en· Journal of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
124
citations
affunlabeled
The set covering machine
Mario Marchand, John Shawe‐Taylor
2003· article· en· ePrints Soton (University of Southampton)· Computer Science
machine prediction:candidate · noneconsensus · none
112
citations
affunlabeled
XTRACT
Minos Garofalakis, Aristides Gionis, Rajeev Rastogi, S. Seshadri, Kyuseok Shim
2000· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
102
citations
affunlabeled
Gated Orthogonal Recurrent Units: On Learning to Forget
Li Jing, Çağlar Gülçehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljačić +1 more
2019· article· en· Neural Computation· Computer Science
machine prediction:candidate · noneconsensus · none
101
citations
affunlabeled
Agnostic Online Learning.
Shai Ben-David, Dávid Pál, Shai Shalev‐Shwartz
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
93
citations
affunlabeled
Mine Classification With Imbalanced Data
David P. Williams, Vincent Myers, Miranda Schatten Silvious
2009· article· en· IEEE Geoscience and Remote Sensing Letters· Computer Science
machine prediction:candidate · noneconsensus · none
89
citations
affunlabeled
Budgeted learning of nailve-bayes classifiers
Daniel J. Lizotte, Omid Madani, Russell Greiner
2002· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
77
citations
afffundunlabeled
A General Theory of Additive State Space Abstractions
Fan Yang, Joseph Culberson, Robert C. Holte, Uzi Zahavi, Ariel Felner
2008· article· en· Journal of Artificial Intelligence Research· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
affno abstractunlabeled
Learnability can be undecidable
Shai Ben-David, Pavel Hrubeš, Shay Moran, Amir Yehudayoff
2018· article· en· Nature Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
73
citations
affunlabeled
Artificial Intelligence Review
Amal Kilani, Ahmed Ben Hamida, Habib Hamam
2018· book-chapter· en· Advances in computer and electrical engineering book series· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affunlabeled
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
2017· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
afffundno abstractunlabeled
Covering Things with Things
Stefan Langerman, Pat Morin
2004· article· en· Discrete & Computational Geometry· Computer Science
machine prediction:candidate · noneconsensus · none
65
citations
afffundno abstractunlabeled
Learning and Classifying Under Hard Budgets
Aloak Kapoor, Russell Greiner
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations
afffundno abstractunlabeled
Decision Tree Instability and Active Learning
Kenneth Dwyer, Robert C. Holte
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations
affno abstractunlabeled
Learning to Learn with Conditional Class Dependencies
Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin
2018· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
Learning Algorithms for Active Learning
Philip Bachman, Alessandro Sordoni, Adam Trischler
2017· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
Active model selection
Omid Madani, Daniel J. Lizotte, Russell Greiner
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations
afffundno abstractunlabeled
Complexity of barrier coverage with relocatable sensors in the plane
Stefan Dobrev, Stéphane Durocher, Mohsen Eftekhari, Konstantinos Georgiou, Evangelos Kranakis, Danny Kriz̧anc +4 more
2015· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
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