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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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Public Health Policies and Education
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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.

1,492 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.
1,492 works in the cohort · of 4,299,418page 17 of 30

Labels cover 10 of 1,492 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 1,492 of 1,492 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.

affunlabeled
Rejoinder
David W. Dowdy, Madhukar Pai
2012· letter· fr· Epidemiology· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
venueaboutno affunlabeled
Editorial: Where Do We Go from Here?
Marianne Trevorrow
2022· editorial· en· CAND Journal· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Value Systems and Healthcare Ethics
Bernard M. Dickens
2008· book-chapter· en· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
APPENDICES
2013· article· fr· Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
aboutno affno abstractunlabeled
A Canadian School for Sure
Marylin J. McKay
2011· book-chapter· en· McGill-Queen's University Press eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
ForumSpace
The Meducator, Chloe Gao, Kartik Sharma, Peter Belesiotis
2019· article· en· The Meducator· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Message from the president.
D D Smyth
2001· article· en· PubMed· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
From “learning from the field” to jointly driving change
Joshua Galjour, Thomas Schwarz, Itai Rusike, Marta Lomazzi, Laura Hoemeke, Helen Prytherch +10 more
2021· review· en· Journal of Public Health Policy· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About