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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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BMC Bioinformatics
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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.

647 results · 1 filter active ·
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20022025
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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.
647 works in the cohort · of 4,299,418page 3 of 13

Labels cover 1 of 647 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 647 of 647 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 network model for angiogenesis in ovarian cancer
Kimberly Glass, John Quackenbush, Dimitrios Spentzos, Benjamin Haibe‐Kains, Guo‐Cheng Yuan
2015· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
73
citations
afffundunlabeled
A methodology for global validation of microarray experiments
Mathieu Miron, Owen Z. Woody, Alexandre Marcil, Carl Murie, Robert Sladek, Robert Nadon
2006· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
72
citations
afffundunlabeled
A machine learning approach for viral genome classification
Mohamed Amine Remita, Ahmed Halioui, Abou Abdallah Malick Diouara, Bruno Daigle, Golrokh Kiani, Abdoulaye Baniré Diallo
2017· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
69
citations
affunlabeled
Relations as patterns: bridging the gap between OBO and OWL
Robert Hoehndorf, Anika Oellrich, Michel Dumontier, Janet Kelso, Dietrich Rebholz‐Schuhmann, Heinrich Herre
2010· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
66
citations
affunlabeled
Recrafting the neighbor-joining method
Thomas Mailund, Gerth Stølting Brodal, Rolf Fagerberg, Christian NS Pedersen, Derek Phillips
2006· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
61
citations
affunlabeled
A better sequence-read simulator program for metagenomics
Stephen E. Johnson, Brett Trost, Jeffrey R Long, Vanessa Pittet, Anthony Kusalik
2014· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
53
citations
afffundunlabeled
Filtering Genes for Cluster and Network Analysis
David Tritchler, Elena Parkhomenko, Joseph Beyene
2009· article· en· BMC Bioinformatics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
51
citations

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