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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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Artificial Intelligence in Healthcare and Education
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,498 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.
3,498 works in the cohort · of 4,299,418page 55 of 70

Labels cover 44 of 3,498 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 3,498 of 3,498 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
AI in Higher Education Innovation Exchange
Alexandra Poppendorf, Mohammadmahdi Zanjanian, Bridgette Crabbe
2025· book· en· University of Calgary eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
afffundvenueaboutno abstractunlabeled
“A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform [Canadian Journal of Cardiology Volume 40, Issue 10, October 2024, Pages 1828-1840]”
Pascal Thériault-Lauzier, Denis Corbin, Olivier Tastet, Élodie Labrecque Langlais, Bahareh Taji, Guson Kang +9 more
2025· erratum· en· Canadian Journal of Cardiology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Message from the Editor-in-Chief
Dapeng Wu
2025· article· en· Transactions on Artificial Intelligence· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The 2024 generation
Shruthy Suresh Aggarwal, Cristina Andreani, Zihou Deng, Julia Frede, Polina Kameneva, Marta Kovatcheva +4 more
2024· article· en· Nature Cancer· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Reply
Carrie Ye
2024· letter· en· Arthritis & Rheumatology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Search strategy.
2024· article· en· Figshare· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Fundamentals of artificial intelligence
Parsa Bagherzadeh, Laya Rafiee Sevyeri, Yujing Zou, Shirin A. Enger
2025· book-chapter· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About