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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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AI in cancer detection
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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.

1,463 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.
1,463 works in the cohort · of 4,299,418page 1 of 30

Labels cover 4 of 1,463 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 1,463 of 1,463 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
Segment anything in medical images
Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, Bo Wang
2024· article· en· Nature Communications· Computer Science
distilled prediction:candidate · noneconsensus · none
2,390
citations
afffundunlabeled
Deep Learning: A Primer for Radiologists
Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher Pal +2 more
2017· review· en· Radiographics· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1,099
citations
affno abstractunlabeled
What Is Machine Learning?
Issam El Naqa, Martin J. Murphy
2015· book-chapter· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
752
citations
affno abstractunlabeled
Diffusion models in medical imaging: A comprehensive survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad, Mohsen Fayyaz, Ilker Hacihaliloglu +1 more
2023· review· en· Medical Image Analysis· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
652
citations
afffundunlabeled
Review of the current state of whole slide imaging in pathology
Liron Pantanowitz, Paul N. Valenstein, Andrew Evans, Keith J. Kaplan, John D. Pfeifer, David C. Wilbur +2 more
2011· article· en· Journal of Pathology Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
432
citations
affno abstractunlabeled
Pattern Classification
Thanh M. Cabral, Rangaraj M. Rangayyan
2012· book-chapter· en· Synthesis lectures on biomedical engineering· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
429
citations
affunlabeled
Automatic Identification of the Pectoral Muscle in Mammograms
Ricardo J. Ferrari, Rangaraj M. Rangayyan, J. E. Leo Desautels, R. A. Borges, A.F. Frere
2004· article· en· IEEE Transactions on Medical Imaging· Computer Science
distilled prediction:candidate · noneconsensus · none
211
citations

How this was built: Screen · Findings · About