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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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Health disparities and outcomes
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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.

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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.

4,167 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.
4,167 works in the cohort · of 4,299,418page 63 of 84

Labels cover 28 of 4,167 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 4,167 of 4,167 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
Why are so many Canadians dying?
Carolyn Brown
2024· article· en· BMJ· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Age, Period, and Cohort Effects
Ethan Fosse
2025· other· en· The Blackwell Encyclopedia of Sociology· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
How Age-Friendly Are Cities?
Lucie Vidovićová
2016· book-chapter· en· Advances in data mining and database management book series· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
FAMILIES AND DEMENTIA: ESTIMATES AND EXPOSURES
Esther M. Friedman, Vicki A. Freedman, Sarah Patterson
2023· article· en· Innovation in Aging· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Health Status Instruments, Measurement Properties of
Holger J. Schünemann, Elizabeth F. Juniper, Gordon Guyatt
2014· other· en· Wiley StatsRef: Statistics Reference Online· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
MEASURING COGNITION IN NSHAP USING MULTIMODE DATA COLLECTION
Kelly Pudelek, Henrique Ochoa Scussiatto, L. Philip Schumm, Kristen Wroblewski, Selena Zhong, Meiyi Li
2023· article· en· Innovation in Aging· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Public Health and Social Policy
Sarah Sanford, Brenda Roche, Kwame McKenzie
2025· book-chapter· en· Oxford University Press eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
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