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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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Advanced Multi-Objective Optimization 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.

645 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.
645 works in the cohort · of 4,299,418page 12 of 13

Labels cover 0 of 645 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 645 of 645 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
Data-Driven Inverse Optimization
Taewoo Lee, Daria Terekhov
2022· book-chapter· en· Encyclopedia of Optimization· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Automated Model Tuning Using A Genetic Algorithm
Suzanne Swaine, Robert Langlois
2016· article· en· Proceedings of the International Conference of Control, Dynamic systems, and Robotics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Optimal Design for Least Squares Estimators
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Characterizing Global Behavior of Approximation Functions
Jocelyn Bourgeois, Jean‐Yves Trépanier, François Guibault, Christophe Tribes
2007· article· en· 45th AIAA Aerospace Sciences Meeting and Exhibit· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Discussion (3): Jones–Johnson Paper
Jason L. Loeppky, Brian J. Williams
2009· article· en· Quality and Reliability Engineering International· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
<scp>P</scp> areto Distribution
Aaron Childs, N. Balakrishnan
2005· other· en· Encyclopedia of Biostatistics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Reference Point-Based Particle Sub-Swarm Optimization
Benjamin DeBoer, Conor McDermott, Ali Hosseini, Carlos Rossa
2021· article· en· 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Randomized Optimization
Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev
2011· other· en· Wiley series in probability and statistics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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