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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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Metaheuristic Optimization Algorithms Research
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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.

762 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.
762 works in the cohort · of 4,299,418page 1 of 16

Labels cover 0 of 762 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 762 of 762 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
– Ant System
Thomas Stützle, Holger H. Hoos
2000· article· en· Future Generation Computer Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2,698
citations
affunlabeled
Opposition-Based Differential Evolution
Shahryar Rahnamayan, Hamid R. Tizhoosh, M.M.A. Salama
2008· article· en· IEEE Transactions on Evolutionary Computation· Computer Science
machine prediction:candidate · noneconsensus · none
1,602
citations
affno abstractunlabeled
Opposition based learning: A literature review
Sedigheh Mahdavi, Shahryar Rahnamayan, Kalyanmoy Deb
2017· review· en· Swarm and Evolutionary Computation· Computer Science
machine prediction:candidate · noneconsensus · none
465
citations
affunlabeled
Quasi-oppositional Differential Evolution
Shahryar Rahnamayan, Hamid R. Tizhoosh, M.M.A. Salama
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
447
citations
affunlabeled
Gaussian Bare-Bones Differential Evolution
Hui Wang, Shahryar Rahnamayan, Hui Sun, Mahamed G. H. Omran
2012· article· en· IEEE Transactions on Cybernetics· Computer Science
machine prediction:candidate · noneconsensus · none
268
citations
affno abstractunlabeled
Multi-strategy ensemble artificial bee colony algorithm
Hui Wang, Zhijian Wu, Shahryar Rahnamayan, Hui Sun, Yong Liu, Jeng‐Shyang Pan
2014· article· en· Information Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
260
citations
fundno affunlabeled
Differential Evolution With Two-Level Parameter Adaptation
Wei–Jie Yu, Meie Shen, Wei–Neng Chen, Zhi‐Hui Zhan, Yue‐Jiao Gong, Ying Lin +2 more
2014· article· en· IEEE Transactions on Cybernetics· Computer Science
machine prediction:candidate · noneconsensus · none
226
citations
affno abstractunlabeled
Opposition-Based Differential Evolution
Shahryar Rahnamayan, Hamid R. Tizhoosh, M.M.A. Salama
2008· book-chapter· en· Studies in computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
123
citations
affno abstractunlabeled
First vs. best improvement: An empirical study
Pierre Hansen, Nenad Mladenović
2005· article· en· Discrete Applied Mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
115
citations
affno abstractunlabeled
Using Genetic Algorithms to Optimize ACS-TSP
Marcin L. Pilat, Tony White
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
96
citations

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