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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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International Journal for Population Data Science
Topic
Retraction
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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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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

810 results · 1 filter active ·
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20172025
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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.
810 works in the cohort · of 4,299,418page 13 of 17

Labels cover 7 of 810 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 810 of 810 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affaboutunlabeled
Facilitating Patient Recruitment Process for Research
Bing Li, Braden Manns, Jim Raso, Terry Saunders, Jeffrey A. Bakal
2018· article· en· International Journal for Population Data Science· Medicine
distilled prediction:candidate · metaresearchconsensus · none
0
citations
aboutno affunlabeled
Better decision making practices and processes.
Felicity Flack, Carolyn Adams
2022· article· en· International Journal for Population Data Science· Medicine
distilled prediction:candidate · metaresearchconsensus · none
0
citations
affaboutunlabeled
What makes great data documentation?
Mark Smith, Mahmoud Azimaee
2018· article· en· International Journal for Population Data Science· Social Sciences
distilled prediction:candidate · sts+scholarly_communicationconsensus · scholarly_communication
0
citations
affunlabeled
Conceptualizing community data governance for race-related, population data: a scoping review and key informant interviews
Elise Leong-Sit, Laura Legere, Sabella Yussuf-Homenauth, Astrid Guttmann, Baiju R. Shah, Michael J. Schull +2 more
2024· review· en· International Journal for Population Data Science· Decision Sciences
distilled prediction:candidate · metaresearch+metaepi_narrow+scholarly_communication+open_scienceconsensus · metaresearch+scholarly_communication+open_science
0
citations
affaboutunlabeled
Involving the Public in Data Linkage Research
Mhairi Aitken, Annette Braunack‐Mayer, Felicity Flack, Kimberlyn McGrail, Michael Burgess, P. Alison Paprica
2020· article· en· International Journal for Population Data Science· Medicine
distilled prediction:candidate · metaresearch+open_scienceconsensus · none
0
citations
aboutno affunlabeled
ICES Data and Analytic Services: Eight Years Young.
Minnie Ho, Stefana Jovanovska, Jenna Novess, Dina Skvirsky, Refik Saskin, J. Charles Victor
2022· article· en· International Journal for Population Data Science· Social Sciences
distilled prediction:candidate · stsconsensus · none
0
citations
affaboutunlabeled
Mapping where patients access primary care providers.
Eliot Frymire, Peter Gozdyra, Michael Green, Imaan Bayoumi, Richard H. Glazier, Liisa Jaakkimainen +3 more
2022· article· en· International Journal for Population Data Science· Health Professions
distilled prediction:candidate · stsconsensus · none
0
citations
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How this was built: Screen · Findings · About