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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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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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

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 6 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
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
affno abstractunlabeled
Automatic learning of subclasses of pattern languages
John Case, Sanjay Jain, Trong Dao Le, Yuh Shin Ong, Pavel Semukhin, Frank Stephan
2012· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Active Learning for Matching Problems
Laurent Charlin, Craig Boutilier
2012· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Fast cross-validation for incremental learning
Pooria Joulani, András György, Csaba Szepesvári
2015· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Automatic Learning of Subclasses of Pattern Languages
John Case, Sanjay Jain, Trong Dao Le, Yuh Shin Ong, Pavel Semukhin, Frank Stephan
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
fundno affno abstractunlabeled
Query learning of bounded-width OBDDs
Atsuyoshi Nakamura
2000· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe‐Taylor
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Probably Approximately Correct Heuristic Search
Roni Stern, Ariel Felner, Robert C. Holte
2021· article· en· Proceedings of the International Symposium on Combinatorial Search· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Algebraic generalization
Stephen M. Watt
2005· article· en· ACM SIGSAM Bulletin· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
Better Peer Grading through Bayesian Inference
Hedayat Zarkoob, Greg d'Eon, Lena Podina, Kevin Leyton‐Brown
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Sauer’s Bound for a Notion of Teaching Complexity
Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Learning Communicating State Machines
Alexandre Petrenko, Florent Avellaneda
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Learning expressions and programs over monoids
Ricard Gavaldà, Pascal Tesson, Denis Thérien
2005· article· en· Information and Computation· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
The Lower Reaches of Circuit Uniformity
Christoph Behle, Andreas Krebs, Klaus-Jörn Lange, Pierre McKenzie
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Sample Compression for Multi-label Concept Classes
Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles
2014· article· en· Conference on Learning Theory· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Learning Linear Programs from Optimal Decisions
Yingcong Tan, Daria Terekhov, Andrew Delong
2020· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
[no title]
Clément L. Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl Wimmer
2019· article· en· Theory of Computing· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
4
citations
affno abstractunlabeled
Training Linear Finite-State Machines.
Arash Ardakani, Amir Ardakani, Warren J. Gross
2020· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affno abstractunlabeled
On learning of functions refutably
Sanjay Jain, Efim Kinber, Rolf Wiehagen, Thomas Zeugmann
2003· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

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