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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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Occupational Health and Safety Research
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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.

2,761 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
2,761 works in the cohort · of 4,299,418page 43 of 56

Labels cover 8 of 2,761 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 2,761 of 2,761 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
CSA EXP248: Pipeline Human Factors
Lorna Harron, Sue Capper
2016· article· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Review of OA in developing countries
Peter Suber
2005· preprint· en· Health Professions
machine prediction:candidate · open_scienceconsensus · none
0
citations
aboutno affunlabeled
Overview of OA in Brazil
Gavin Baker
2008· preprint· en· Health Professions
machine prediction:candidate · scholarly_communication+open_scienceconsensus · none
0
citations
affunlabeled
Burnout and Workplace Injuries
Michael P. Leiter, Christina Maslach
2009· book-chapter· en· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Principal
AB Collins, D Alcock
2012· article· en· Injury Prevention· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Hazard Index (HI)
Monica Nordberg, Douglas M. Templeton, Ole Andersen, John H. Duffus
2016· dataset· en· IUPAC Standards Online· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
TMS et facteurs psychosociaux
2013· paratext· fr· Perspectives interdisciplinaires sur le travail et la santé· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About