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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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Medical Education and Admissions
Retraction
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

676 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.
676 works in the cohort · of 4,299,418page 12 of 14

Labels cover 5 of 676 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 676 of 676 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.

aboutno affunlabeled
Mircea Cantor
2019· book· en· E-Artexte (Artexte)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Perennial post-examination surprises
Kendall Noel
2024· article· en· Canadian Family Physician· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
9789461663948.pdf
2021· other· en· OAPEN (The OAPEN Foundation)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Administrative Trends in U.S. Dental Schools
Martin M. Fu, Ángel Emmanuel Rodríguez, Rebecca Y. Chen, Earl Fu, Shu‐Yi Liao, Nadeem Y. Karimbux
2014· article· en· Journal of Dental Education· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
International Briefs
2006· article· en· Journal of Medical Regulation· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
From neural to social
Shane Dawson, Leah P. Macfadyen, Lori Lockyer, David Mazzochi-Jones
2010· article· en· ASCILITE Publications· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About