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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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Machine Learning and Data Classification
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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.

559 results · 1 filter active ·
Categories
Machine labels · sparse coverage
Evidence
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
559 works in the cohort · of 4,299,418page 4 of 12

Labels cover 1 of 559 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 559 of 559 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Forgetting Reinforced Cases
2008· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
12
citations
afffundno abstractunlabeled
Granular counting of uncertain data
2019· article· en· Fuzzy Sets and Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Supervised Learning with Minimal Effort
2010· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
11
citations
affno abstractunlabeled
Semisupervised learning methods
2024· book-chapter· en· Elsevier eBooks· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
11
citations
fundno affunlabeled
GeneCAI
2020· preprint· en· Computer Science
distilled prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
AutoML @ NeurIPS 2018 Challenge: Design and Results
2019· book-chapter· en· ˜The œSpringer series on challenges in machine learning· Computer Science
distilled prediction:candidate · metaepi_narrow+research_integrityconsensus · none
10
citations
affunlabeled
Herded gibbs sampling
2016· article· en· UvA-DARE (University of Amsterdam)· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Consistent Subset Problem with Two Labels
2018· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
affunlabeled
Classification Methods
2005· book-chapter· en· IGI Global eBooks· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
7
citations
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