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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.

This cohort has 4,299,418 works, more than the 100,000-row export cap: the file will contain the first 100,000 ordered by OpenAlex id, and says so in its last line. Narrow the cohort, page the API, or rebuild the frame from the repository for the rest. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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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.

4,299,418 results · 0 filters 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.
4,299,418 works in the cohort · of 4,299,418page 606 of 85,989

Labels cover 11,048 of 4,299,418 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 4,299,418 of 4,299,418 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.

affunlabeled
Participatory Action Research
Jacques Chevalier, Daniel J. Buckles
2019· book· en· Social Sciences
machine prediction:candidate · noneconsensus · none
377
citations
affno abstractunlabeled
Improving environmental awareness training in business
Geneviève M. Perron, Raymond P. Côté, John Duffy
2005· article· en· Journal of Cleaner Production· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
376
citations
affunlabeled
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram +2 more
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
376
citations
affunlabeled
Fuzzy EMG classification for prosthesis control
F.H.Y. Chan, Yongsheng Yang, F.K. Lam, Yuan‐Ting Zhang, P.A. Parker
2000· article· en· IEEE Transactions on Rehabilitation Engineering· Engineering
machine prediction:candidate · noneconsensus · none
376
citations
affunlabeled
Psoriasis: classical and emerging comorbidities
Maria de Fátima Santos Paim de Oliveira, Bruno de Oliveira Rocha, Gleison Vieira Duarte
2015· review· en· Anais Brasileiros de Dermatologia· Immunology and Microbiology
machine prediction:candidate · noneconsensus · none
376
citations
affno abstractunlabeled
GlobalSoilMap
Dominique Arrouays, Alfred E. Hartemink, Jonathan Hempel, G.B.M. Heuvelink, S. Young Hong, Philippe Lagacherie +10 more
2014· book-chapter· en· Advances in agronomy· Environmental Science
machine prediction:candidate · noneconsensus · none
376
citations
affno abstractunlabeled
Insulins today and beyond
David R. Owens, Bernard Zinman, Geremia B. Bolli
2001· review· en· The Lancet· Medicine
machine prediction:candidate · noneconsensus · none
376
citations
affaboutunlabeled
New-Onset Atrial Fibrillation
Karin H. Humphries, Charles R. Kerr, Stuart J. Connolly, George J. Klein, John A. Boone, Martin Green +4 more
2001· article· en· Circulation· Medicine
machine prediction:candidate · noneconsensus · none
376
citations

How this was built: Screen · Findings · About