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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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Evolutionary Algorithms and Applications
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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.

691 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.
691 works in the cohort · of 4,299,418page 5 of 14

Labels cover 0 of 691 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 691 of 691 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
A Review of Thresheld Convergence
Stephen Chen, James Montgomery, Antonio Bolufé-Röhler, Yasser González-Fernández
2015· review· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Unary Patterns with Permutations
James D. Currie, Florín Manea, Dirk Nowotka
2015· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Deep Learning Concepts for Evolutionary Art
Fazle Tanjil, Brian J. Ross
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
NEW PROBLEMS OF PATTERN AVOIDANCE
John Patrick Loftus, Jeffrey Shallit, Mingwei Wang
2000· article· en· Developments in Language Theory· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Plant Tissue Modelling Using Power-Law Filters
Samar I. Gadallah, Mohamed S. Ghoneim, Ahmed S. Elwakil, Lobna A. Said, Ahmed H. Madian, Ahmed G. Radwan
2022· article· en· Sensors· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
venueno affno abstractunlabeled
Evolutionary Graphs on Two Levels.
Pu‐yan Nie
2008· article· en· Ars Combinatoria· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Using Evolvable Regressors to Partition Data
Joseph Alexander Brown, Daniel Ashlock
2010· book-chapter· en· ASME Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
fundno affno abstractunlabeled
Applications of Evolutionary Computation
2024· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Evolvable Metaheuristics on Circuit Design
Felipe Padilla, Aurora Torres Soto, Julio Ponce, Mara Dolores, Sylvie Ratt, Eunice Ponce-de-Le
2011· book-chapter· en· InTech eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
A Game Theoretic Approach to Swarm Robotics
Sidney Givigi, Howard M. Schwartz
2006· article· en· Applied Bionics and Biomechanics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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