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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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Statistical and Computational Modeling
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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.

88 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.
88 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 88 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 88 of 88 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
Conceptual Modeling – ER 2010
Jeffrey Parsons, Yair Wand, Carson Woo, Peretz Shoval, Motoshi Saeki
2010· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
94
citations
affunlabeled
GMDH-Methodology and Implementation in C
Godfrey C. Onwubolu
2011· book· en· IMPERIAL COLLEGE PRESS eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Hybrid Differential Evolution and GMDH Systems
Godfrey C. Onwubolu
2009· book-chapter· en· Studies in computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Improving the efficiency of nonparametric entropy estimation
Е. А. Тимофеев, Alexei Kaltchenko
2014· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affno abstractunlabeled
Halifax OK's $40,000 solo trash study
2022· other· en· Saint Mary's University Institutional Repository (Saint Mary's University)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Problem Formulation (in Ecological Risk Assessment)
Monica Nordberg, Douglas M. Templeton, Ole Andersen, John H. Duffus
2016· dataset· en· IUPAC Standards Online· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Synergism (in Toxicology)
John H. Duffus, Monica Nordberg, Douglas M. Templeton
2016· dataset· en· IUPAC Standards Online· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Метод матричних d-дерев і його застосування до символьного аналізу лінійних параметричних кіл у частотній області
Юрій Іванович Шаповалов, Дарія Романівна Бачик, Ксенія Олегівна Децик, Роман Романюк, Іван Юрійович Шаповалов
2022· article· uk· Известия высших учебных заведений Радиоэлектроника· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About