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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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Studies in classification, data analysis, and knowledge organization
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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.

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

Labels cover 0 of 27 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 27 of 27 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
Locally Linear Regression and the Calibration Problem for Micro-Array Analysis
Antonio Ciampi, Benjamin Rich, Alina Dyachenko, Isadora Antoniano‐Villalobos, Carl Murie, Robert Nadon
2007· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
A Family of Average Consensus Methods for Weighted Trees
Claudine Levasseur, François‐Joseph Lapointe
2002· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
A Fast Electric Vehicle Planner Using Clustering
Jaël Champagne Gareau, Éric Beaudry, Vladimir Makarenkov
2021· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
A New Metric to Classify B Cell Lineage Tree
Mahsa Farnia, Nadia Tahiri
2025· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Statistical Profiling of Hybrid CNN-SVM Effectiveness
Abdallah Benkadja, Alaidine Ben Ayed, Ismaïl Biskri, Nadia Ghazzali
2024· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Virtual High Throughput Screening Using Machine Learning Methods
Chérif Mballo, Vladimir Makarenkov
2010· book-chapter· en· Studies in classification, data analysis, and knowledge organization· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About