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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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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.

46 results · 1 filter active ·
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20012025
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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.
46 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 46 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 46 of 46 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
Maximum Split Clustering Under Connectivity Constraints
Pierre Hansen, Brigitte Jaumard, Christophe Meyer, Bruno Simeone, Valeria Doring
2003· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Fractionally-Supervised Classification
2015· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
The Metric Cutpoint Partition Problem
Alain Hertz, Sacha Varone
2008· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Model Selection for the Trend Vector Model
Hsiu‐Ting Yu, Mark de Rooij
2013· article· en· Journal of Classification· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
On Assessments of Agreement Between Fuzzy Partitions
Jeffrey L. Andrews, Ryan P. Browne, Chelsey D. Hvingelby
2022· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Chimeral Clustering
Jason Hou-Liu, Ryan P. Browne
2021· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Editorial: Journal of Classification Vol. 41-1
Paul D. McNicholas
2024· editorial· en· Journal of Classification· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Editorial: Journal of Classification Vol. 39-1
Paul D. McNicholas
2022· editorial· en· Journal of Classification· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Editorial: Journal of Classification Vol. 41-3
Mark de Rooij, Brian C. Franczak, Cinzia Viroli, Arthur White, Paul D. McNicholas
2024· editorial· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Matrix Normal Cluster-Weighted Models
Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo
2021· preprint· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Editorial: Journal of Classification Vol. 37-2
Paul D. McNicholas, Douglas Steinley
2020· editorial· en· Journal of Classification· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
0
citations

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