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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
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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 4 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
FSM Inference from Long Traces
Florent Avellaneda, Alexandre Petrenko
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
Amortized Bayesian Optimization over Discrete Spaces
Kevin Swersky, Yulia Rubanova, David Dohan, Kevin J. Murphy
2020· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Data sparseness in linear SVM
Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling
2015· article· en· International Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
afffundunlabeled
GP under streaming data constraints
Aaron Atwater, Malcolm I. Heywood, A. Nur Zincir‐Heywood
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
fundno affunlabeled
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Omar Rivasplata, Emilio Parrado-Hernández, John Shawe‐Taylor, Shiliang Sun, Csaba Szepesvári
2018· article· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
afffundunlabeled
Adaptive sampling strategies for quickselects
Conrado Martı́nez, Daniel Panario, Alfredo Viola
2010· article· en· ACM Transactions on Algorithms· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
afffundno abstractunlabeled
Finding hidden independent sets in interval graphs
Thérèse Biedl, Broňa Brejová, Erik D. Demaine, Angèle M. Hamel, Alejandro López-Ortíz, Tomáš Vinař
2003· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
The complexity of estimating min-entropy
Thomas Watson
2014· article· en· Computational Complexity· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
afffundunlabeled
Inferring Symbolic Automata
Dana Fisman, Hadar Frenkel, Sandra Zilles
2023· article· en· Logical Methods in Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
The complexity of learning acyclic CP-nets
Eisa Alanazi, Malek Mouhoub, Sandra Zilles
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Representing systems with hidden state
Christopher Hundt, Prakash Panagaden, Joëlle Pineau, Doina Precup
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Asterisk
Mona Nashaat, Aindrila Ghosh, James Miller, Shaikh Quader
2020· article· en· ACM/IMS Transactions on Data Science· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
10
citations
afffundunlabeled
Finding Nearly Optimal GDT Scores
Shuai Cheng Li, Dongbo Bu, Jinbo Xu, Ming Li
2011· article· en· Journal of Computational Biology· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
afffundno abstractunlabeled
Mind Change Efficient Learning
Wei Luo, Oliver Schulte
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
fundno affunlabeled
Model processing tools in UML
J. Koskinen, J. Peltonen, P. Selonen, T. Systa, K. Koskimies
2005· article· en· Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001· 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
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
affno abstractunlabeled
Fundamentals of Machine Learning
Ke-Lin Du, M. N. S. Swamy
2019· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Probably bounded suboptimal heuristic search
Roni Stern, Gal Dreiman, Richard Valenzano
2018· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
A PAC-Bayes Risk Bound for General Loss Functions
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
2007· book-chapter· en· The MIT Press eBooks· 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
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