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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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Electrospun Nanofibers in Biomedical Applications
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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.

1,209 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.
1,209 works in the cohort · of 4,299,418page 2 of 25

Labels cover 3 of 1,209 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 1,209 of 1,209 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
Introduction to Nanofiber Materials
Frank Ko, Yuqin Wan
2014· book· en· Cambridge University Press eBooks· Materials Science
machine prediction:candidate · noneconsensus · none
147
citations
affunlabeled
Biomaterials and Scaffolds in Reparative Medicine
ELLIO L. CHAIKOF, Howard W.T. Matthew, Joachim Kohn, ANTONIO G. MIKOS, Glenn D. Prestwich, Christopher M. Yip
2002· review· en· Annals of the New York Academy of Sciences· Materials Science
machine prediction:candidate · noneconsensus · none
123
citations
affunlabeled
Biomaterials for Brain Tissue Engineering
Jerani T. S. Pettikiriarachchi, Clare L. Parish, Molly S. Shoichet, John S. Forsythe, David R. Nisbet
2010· article· en· Australian Journal of Chemistry· Materials Science
machine prediction:candidate · noneconsensus · none
115
citations
venueno affunlabeled
Scaffolds in tissue engineering of blood vessels
Divya Pankajakshan, Devendra K. Agrawal
2010· review· en· Canadian Journal of Physiology and Pharmacology· Materials Science
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
From In Vitro to In Situ Tissue Engineering
Debanti Sengupta, Stephen D. Waldman, Li Song
2014· review· en· Annals of Biomedical Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
100
citations

How this was built: Screen · Findings · About