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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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Genetic Associations and Epidemiology
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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.

3,195 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.
3,195 works in the cohort · of 4,299,418page 35 of 64

Labels cover 10 of 3,195 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 3,195 of 3,195 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
Reply to Rational drug repositioning by medical genetics
Philippe Sanséau, Pankaj Agarwal, Michael R. Barnes, Tomi Pastinen, J. Brent Richards, Lon R. Cardon +1 more
2013· letter· en· Nature Biotechnology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Brief Survey on Machine Learning in Epistasis
Davide Chicco, Trent Faultless
2021· review· en· Methods in molecular biology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
venueno affunlabeled
Variance in disease risk: rural populations and genetic diversity
Wiley D. Jenkins, Alexander E. Lipka, Amanda Fogleman, Kristin Delfino, Ripan S. Malhi, Brian Hendricks
2016· review· en· Genome· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
PedSplit: pedigree management for stratified analysis
Matthew B. Lanktree, L. VanderBeek, Fabìo Macciardi, J.L. Kennedy
2004· article· en· Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
afffundunlabeled
A sequence of methodological changes due to sequencing
Kelly M. Burkett, Celia M.T. Greenwood
2013· review· en· Current Opinion in Allergy and Clinical Immunology· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
fundno affunlabeled
Two Loci Affect Angiotensin I–Converting Enzyme Activity in Baboons
Candace M. Kammerer, David L. Rainwater, Jennifer L. Schneider, Laura A. Cox, Michael C. Mahaney, Jeffrey Rogers +1 more
2003· article· en· Hypertension· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
venueno affunlabeled
Molecular Genetics of the Psychosis Phenotype
Pamela DeRosse, Anil K. Malhotra, Todd Lencz
2012· review· en· The Canadian Journal of Psychiatry· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations
fundno affunlabeled
Genetically proxied impaired GIPR signaling and risk of 6 cancers
Miranda J. Rogers, Dipender Gill, Emma Ahlqvist, Tim Robinson, Daniela Mariosa, Mattias Johansson +7 more
2023· article· en· iScience· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About