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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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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.

96 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
96 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 96 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 96 of 96 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
Collaborative fuzzy clustering
Witold Pedrycz
2002· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
282
citations
affno abstractunlabeled
An objective approach to cluster validation
Mohamed Bouguessa, Shengrui Wang, Haojun Sun
2006· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
122
citations
afffundno abstractunlabeled
Recognizing affect in human touch of a robot
Kerem Altun, Karon E. MacLean
2014· article· en· Pattern Recognition Letters· Psychology
machine prediction:candidate · noneconsensus · none
56
citations
affno abstractunlabeled
Entropy-based representation of image information
Mario Ferraro, Giuseppe Boccignone, Terry Caelli
2002· article· en· Pattern Recognition Letters· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Image thresholding based on semivariance
M. Beauchemin
2012· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Guided Locally Linear Embedding
Babak Alipanahi, Ali Ghodsi
2011· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Logic-oriented fuzzy clustering
Witold Pedrycz, George Vukovich
2002· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Bi-discriminator GAN for tabular data synthesis
Mohammad Esmaeilpour, Nourhene Chaalia, Adel Abusitta, Franşois-Xavier Devailly, Wissem Maazoun, Patrick Cardinal
2022· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Order preserving pattern matching revisited
Md. Mahbubul Hasan, A. S. M. Shohidull Islam, Mohammad Saifur Rahman, M. Sohel Rahman
2014· article· en· Pattern Recognition Letters· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations

How this was built: Screen · Findings · About