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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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Blind Source Separation Techniques
Retraction
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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.

744 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.
744 works in the cohort · of 4,299,418page 1 of 15

Labels cover 1 of 744 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 744 of 744 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.

aboutno affunlabeled
The Transforms and Applications Handbook, Second Edition
2000· book· en· ˜The œelectrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook· Computer Science
machine prediction:candidate · noneconsensus · none
640
citations
afffundunlabeled
The Cocktail Party Problem
S. Haykin, Zhe Chen
2005· review· en· Neural Computation· Computer Science
machine prediction:candidate · noneconsensus · none
515
citations
aboutno affunlabeled
THE CO-INFORMATION LATTICE
Anthony J. Bell
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
168
citations
affunlabeled
Sparse multichannel blind deconvolution
Nasser Kazemi, Mauricio D. Sacchi
2014· article· en· Geophysics· Computer Science
machine prediction:candidate · noneconsensus · none
127
citations
affunlabeled
Fetal MEG Redistribution by Projection Operators
Jan Vrba, Stephen E. Robinson, J. Adam McCubbin, Curtis L. Lowery, Hari Eswaran, James D. Wilson +1 more
2004· article· en· IEEE Transactions on Biomedical Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
afffundunlabeled
Extracting Spread-Spectrum Hidden Data From Digital Media
Ming Li, Michel Kulhandjian, Dimitris A. Pados, Stella N. Batalama, Michael J. Medley
2013· article· en· IEEE Transactions on Information Forensics and Security· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations
affunlabeled
Tuning-free step-size adaptation
Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations
affunlabeled
Optimal reduction of MCG in fetal MEG recordings
J. Adam McCubbin, Stephen E. Robinson, Robert Cropp, A. Moiseev, Jan Vrba, Pam Murphy +2 more
2006· article· en· IEEE Transactions on Biomedical Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affno abstractunlabeled
Principal Component Analysis
Ke-Lin Du, M. N. S. Swamy
2013· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations

How this was built: Screen · Findings · About