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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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Innovation in Aging
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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.

2,424 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.
2,424 works in the cohort · of 4,299,418page 22 of 49

Labels cover 3 of 2,424 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 2,424 of 2,424 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.

affaboutunlabeled
SENIOR HIGH COST HEALTHCARE USERS: HOW DO THEY DIFFER?
Janice Lee, S. Muratov, Jean‐Éric Tarride, Michael J. Paterson, Tara Gomes, Wayne Khuu +1 more
2017· article· en· Innovation in Aging· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Capacity Assessment Training and Competency Evaluation Tool
Lindsey Jacobs, Patricia M. Bamonti, Jessica Strong, Kyle S. Page, Barry A. Edelstein, Rebecca S. Allen +1 more
2020· article· en· Innovation in Aging· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Precarious Aging: A Working Definition
Amanda Grenier, Chris Phillipson, Grace Martin, Abiraa Karalasingam, Karen Kobayashi, Patrik Marier +1 more
2021· article· en· Innovation in Aging· Psychology
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
EXAMINING COVID-19 VACCINE–RELATED AGEISM IN TWITTER DATA
Juanita-Dawne Bacsu, Megan E. O’Connell, Allison Cammer, Alison L. Chasteen, Sarah Fraser, Mehrnoosh Azizi +2 more
2023· article· en· Innovation in Aging· 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
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