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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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Biomedical Text Mining and Ontologies
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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
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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.

1,581 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.
1,581 works in the cohort · of 4,299,418page 10 of 32

Labels cover 8 of 1,581 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 1,581 of 1,581 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
A taxonomy of disposition-parthood
Adrien Barton, Ludger Jansen, Jean‐François Éthier
2017· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
9
citations
fundno affunlabeled
Expert-novice differences in mammogram interpretation
Roger Azevedo, Sonia Faremo, Susanne P. Lajoie
2007· article· en· eScholarship (California Digital Library)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Benchmarking infrastructure for mutation text mining
Artjom Klein, Alexandre Riazanov, Matthew Hindle, Christopher J. O. Baker
2014· article· en· Journal of Biomedical Semantics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affvenueunlabeled
Yale MeSH Analyzer
Robyn Hocking
2017· article· en· Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
8
citations
affunlabeled
Modeling Life Science Knowledge with OWL 1.1.
Michel Dumontier, Natalia Villanueva‐Rosales
2008· article· en· Research Publications (Maastricht University)· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
fundno affunlabeled
Hypothesis, analysis and synthesis, it's all Greek to me
Ioannis Iliopoulos, Sophia Ananiadou, Antoine Danchin, John P. A. Ioannidis, Peter D. Katsikis, Christos Ouzounis +1 more
2019· article· en· eLife· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
MedVLM: Medical Vision–Language Model for Consumer Devices
Muhammad Ayaz, Mustaqeem Khan, Muhammad Saqib, Adel Khelifi, Muhammad Sajjad, Abdulmotaleb Elsaddik
2024· article· en· IEEE Consumer Electronics Magazine· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Sequence Labeling for Disambiguating Medical Abbreviations
Mücahit Çevik, Sanaz Mohammad Jafari, Mitchell Myers, Savaş Yıldırım
2023· article· en· Journal of Healthcare Informatics Research· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About