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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.

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.

691 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.
691 works in the cohort · of 4,299,418page 8 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
Evolving recurrent models using linear GP
Xiao Luo, Malcolm I. Heywood, A. Nur Zincir‐Heywood
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Basic object oriented genetic programming
Tony White, Jinfei Fan, Franz Oppacher
2011· article· en· International Conference Industrial, Engineering & Other Applications Applied Intelligent Systems· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
fundno affno abstractunlabeled
Learning Ranking Functions by Genetic Programming Revisited
Ricardo Baeza‐Yates, Alfredo Cuzzocrea, Domenico Crea, Giovanni Lo Bianco
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
fundno affno abstractunlabeled
Applications of Evolutionary Computation
2025· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Unary patterns under permutations
James D. Currie, Florín Manea, Dirk Nowotka, Kamellia Reshadi
2018· article· en· Theoretical Computer Science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Linear Genetic Programming
Wolfgang Banzhaf, Ting Hu
2025· article· en· Proceedings of the Genetic and Evolutionary Computation Conference Companion· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A single-process learning theory
Marion Blute
2001· article· en· Behavioral and Brain Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Searching and Sorting
Sammie Bae
2019· book-chapter· en· Apress eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Paralog interference preserves genetic redundancy
Angel F. Cisneros, Florian Mattenburger, Isabelle Gagnon‐Arsenault, Yacine Seffal, Lou Nielly-Thibault, Alexandre K. Dubé +3 more
2025· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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How this was built: Screen · Findings · About