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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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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.

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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
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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 20 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.

affno abstractunlabeled
Research Program
2017· article· en· Research Quarterly for Exercise and Sport· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Assessing women veterans’ satisfaction with mobility devices
Diya Kad, Rutuja A. Kulkarni, Kelsey Berryman, Pooja Solanki, Frances M. Weaver, Brad E. Dicianno +1 more
2025· article· en· Journal of Rehabilitation and Assistive Technologies Engineering· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Measuring assistive technology outcomes
Emma Smith, Lorenzo Desideri, Mary Goldberg, W. Ben Mortenson
2025· editorial· en· Assistive Technology· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
List of Contributors
2015· other· en· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Overview of ASSETS 2020
Hugo Nicolau, Karyn Moffatt, Tiago Guerreiro
2021· article· en· ACM SIGACCESS Accessibility and Computing· Health Professions
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Student Competition (Knowledge Generation) ID 1978805
Elina Provad, Tanha Patel, Katherine Chan, Jae Wook Lee, Elizabeth L. Inness, Dalton L. Wolfe +2 more
2023· article· en· Topics in Spinal Cord Injury Rehabilitation· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
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How this was built: Screen · Findings · About