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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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Assistive Technology in Communication and Mobility
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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.

1,042 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.
1,042 works in the cohort · of 4,299,418page 18 of 21

Labels cover 4 of 1,042 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 1,042 of 1,042 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
About the Cover
Uncle Paul Constable Calcott
2022· article· en· Antipodes· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Tempering technology with care
Steven Kirshblum
2012· article· en· Journal of Spinal Cord Medicine· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Correction
Mireille Landry, Dina Brooks, Sherry L. Grace, Laura L. Shoemaker, James Lenker, Marcus J. Führer +2 more
2009· article· en· Archives of Physical Medicine and Rehabilitation· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Research Abstracts
Eric Plotnick
2001· article· en· Quarterly review of distance education· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Acknowledgements
2020· book-chapter· en· McGill-Queen's University Press eBooks· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Technology in Transition to Practice
Judy E. Boychuk Duchscher
2018· article· en· Nursing Education Research Conference 2018 (NERC18)· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
고령자를 위한 일어서기 보조기기 개발 및 평가
김희동, 박제모, 황도연, 이문영, 이두표, 박보라 +1 more
2019· article· ko· The Journal of Korean Society of Occupational Therapy· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The Physical Activity Recall Assessment for Individuals with SCI
Amy E. Latimer‐Cheung, Kathleen A. Martin Ginis, B. Catherine Craven, Geoffrey R. Norman, Audrey L. Hicks
2004· article· en· Medicine & Science in Sports & Exercise· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
If you leave it with me I will work it out
Denise Wood, Parimala Raghavendra, Janelle Sampson, Sheila Scutter, Carolyn Bilsborow, Caitlin Fry +2 more
2013· article· en· Telecommunications Journal of Australia· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About