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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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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
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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.

1,873 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.
1,873 works in the cohort · of 4,299,418page 30 of 38

Labels cover 10 of 1,873 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 1,873 of 1,873 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.

affaboutno abstractunlabeled
Reflections on a tragedy in Canada.
David C. Mendelssohn, Rob Robson
2004· article· en· PubMed· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Playing on OSHA's team.
M P Avery
2003· article· en· PubMed· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Floating Mobile Hospitals
Sabnam Mahmuda, Hasan S. Merali
2018· article· en· Global Health: Annual Review· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
From the Editors
2020· editorial· en· Healthcare Quarterly· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
ICU life lessons
Lana Lovo
2001· article· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
The Baycrest SARS experience: the human side
Jodeme Goldhar, Clare Adie, Nancy Boyd Webb, Laurie R. Harrison
2003· article· en· Australian Health Review· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affvenueaboutunlabeled
SARS
C. Ignacio, M. Jayoma
2004· article· en· Hemodialysis International· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueaboutno affunlabeled
Saviour of lives.
Barbara Sibbald
2001· article· en· Canadian Medical Association Journal· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
542 Mobile Burn Response Teams: A Scoping Review
Danielle Fuchko, Kathryn King‐Shier, Vincent Gabriel
2022· review· en· Journal of Burn Care & Research· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Disasters at Sea
Helge Brändström, Gordon G. Giesbrecht
2014· book-chapter· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(08)70726-2
2000· article· en· Time to knit· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Tiny Virus, Big Picture | The Tyee
Geoff Dembicki
2020· article· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About