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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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Aging, Elder Care, and Social Issues
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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.

2,429 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.
2,429 works in the cohort · of 4,299,418page 17 of 49

Labels cover 6 of 2,429 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 2,429 of 2,429 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.

affvenueno abstractunlabeled
« Les temps changent »!
Janice Munroe
2012· article· fr· The Canadian Journal of Hospital Pharmacy· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Faufil et petit point
Rémi Savard
2007· book· fr· Classiques des sciences sociales.· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Vieillir en Santé ?
Jean‐Pierre Lavoie, Danielle Guay, Norma Gilbert, Manon Parisien
2010· book-chapter· fr· Presses de l'Université du Québec eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affvenueno abstractunlabeled
Présentation
Nicole F. Bernier, Isabelle Mallon
2009· article· fr· Lien social et Politiques· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
venueno affunlabeled
Vieillir, mourir
Louis-Vincent Thomas
2015· article· fr· International Review of Community Development· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Vieillissement, organisation du travail et santé
Hélène David, Francis Derriennic, Esther Cloutier, Serge Volkoff
2003· article· fr· Santé Société et Solidarité· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Chapitre 1. Les grandes tendances sociales
Christophe Jaffrelot
2013· book-chapter· fr· Presses de l’Université de Montréal eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affvenueaboutunlabeled
François chez les vieux
Catherine Grech
2021· article· fr· Theatre Research in Canada· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Le vieillissement au grand âge
Vincent Caradec
2008· article· fr· Sciences Humaines· Health Professions
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About