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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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Ethics in Clinical Research
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
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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.

2,822 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.
2,822 works in the cohort · of 4,299,418page 47 of 57

Labels cover 145 of 2,822 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 2,822 of 2,822 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
European Society of Cardiology
2016· article· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
my illegal research on humans at Ryerson 2.0
Paul Bali
2017· article· en· Humanities Commons CORE (Modern Language Association / Columbia University)· Medicine
machine prediction:candidate · research_integrityconsensus · none
0
citations
affaboutunlabeled
Lessons from the past: A window on the future
Alan Katz, Marni Brownell, Mark Smith
2018· article· en· International Journal for Population Data Science· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundaboutno affno abstractunlabeled
Data ethics and the Canadian Code of Ethics for Psychologists.
Alexis Fabricius, Kieran C. O’Doherty, Jeffery Yen
2025· article· en· Canadian Psychology/Psychologie canadienne· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
0
citations
affno abstractunlabeled
Spread the word on HIV drug
Doug Johnson
2020· article· en· The New Scientist· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About