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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.

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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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Zoonotic diseases and public health
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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
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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.

1,341 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.
1,341 works in the cohort · of 4,299,418page 14 of 27

Labels cover 6 of 1,341 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 1,341 of 1,341 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.

affno abstractunlabeled
Introduction
Roberto Bazzani, M. Wiese
2011· book-chapter· en· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
3
citations
venueno affunlabeled
Veterinary Medicine <i>Is</i> Public Health
Michael J. Blackwell, Rebecca L. Leap
2008· article· en· Journal of Veterinary Medical Education· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Methods for fighting emerging pathogens
Lucas Amenga–Etego, Robin Andersson, Moumita Bhaumik, Young Ki Choi, Hélène Decaluwe, Jemma L. Geoghegan +8 more
2022· article· en· Nature Methods· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Improving food safety in Asia through increased capacity in ecohealth
David C. Hall, Hung Nguyen‐Viet, Iwan Willyanto, Dinh Xuan Tung, Suwit Chotinun
2013· article· en· CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research)· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Submitted poster presentations
2014· article· en· Advances in Animal Biosciences· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
3
citations
afffundunlabeled
Zoonotic Risk Technology Enters the Viral Emergence Toolkit
Colin J. Carlson, Maxwell J. Farrell, Zoë Grange, Barbara A. Han, Nardus Mollentze, Alexandra Phelan +27 more
2021· preprint· en· Preprints.org· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Parasite Zoonoses
L. Polley, Susan Kutz, E.P. Hoberg
2011· book-chapter· en· Elsevier eBooks· Medicine
machine prediction:candidate · noneconsensus · none
3
citations
venueno affgemma · metaresearch+scholarly_communication+research_integritygpt · metaresearch+bibliometrics+scholarly_communication+research_integritymodels split
Hidden Dangers: COVID-19-Based Research in Predatory Journals
Simon Linacre, Sneha K. Rhode, Kathleen Berryman
2024· article· en· Journal of Scholarly Publishing· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
3
citations
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How this was built: Screen · Findings · About