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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
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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 7 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
On the Power of Interactive Proofs for Learning
Tom Gur, Mohammad Mahdi Jahanara, Mohammad Mahdi Khodabandeh, Ninad Rajgopal, Bahar Salamatian, Igor Shinkar
2024· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Min-Max Propagation
Christopher Srinivasa, Inmar E. Givoni, Siamak Ravanbakhsh, Brendan J. Frey
2017· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Improving Co-training with Agreement-Based Sampling
Jin Huang, Jelber Sayyad Shirabad, Stan Matwin, Su Jiang
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Recognizable series on graphs and hypergraphs
Raphaël Bailly, Guillaume Rabusseau, François Denis
2017· article· en· Journal of Computer and System Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
On the teaching complexity of linear sets
Ziyuan Gao, Hans Ulrich Simon, Sandra Zilles
2017· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A faster FPRAS for #NFA
Kuldeep S. Meel, Sourav Chakraborty, Umang Mathur
2024· article· en· Proceedings of the ACM on Management of Data· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A Bayesian neural network for toxicity prediction
Elizaveta Semenova, Dominic P. Williams, Avid M. Afzal, Stanley E. Lazic
2020· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Liu, Ehsan Amjadian, Yuri Lawryshyn, Konstantinos N. Plataniotis
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
UWaterlooMDS at the TREC 2018 Common Core Track.
Mustafa Abualsaud, Gordon V. Cormack, Nimesh Ghelani, Amira Ghenai, Maura R. Grossman, Shahin Rahbariasl +2 more
2018· article· en· Text REtrieval Conference· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
affno abstractunlabeled
Neural ARX Models and PAC Learning
Kayvan Najarian, Guy A. Dumont, M.S. Davies, Nancy Heckman
2000· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Learning with Randomized Majority Votes
Alexandre Lacasse, François Laviolette, Mario Marchand, Francis Turgeon-Boutin
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Sampling heuristics for active function learning
Rebekah Gelpí, Nayan Saxena, George Lifchits, Daphna Buchsbaum, Christopher G. Lucas
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Self-Directed Learner
Eileen A. Ni, Charles X. Ling
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
fundno affno abstractunlabeled
Distinguishing pattern languages with membership examples
Ziyuan Gao, Zeinab Mazadi, Regan Meloche, Hans Ulrich Simon, Sandra Zilles
2017· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Sample-Efficient Learning of Mixtures
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
2018· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Boolean-Arithmetic Equations: Acquisition and Uses
Ramiz Gindullin, Nicolas Beldiceanu, Jovial Cheukam Ngouonou, Rémi Douence, Claude-Guy Quimper
2023· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

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