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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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Advanced Optimization Algorithms Research
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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.

affaffiliation
fundfunder
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aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

604 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.
604 works in the cohort · of 4,299,418page 9 of 13

Labels cover 1 of 604 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 604 of 604 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
QUBO Software
Brad Woods, Gary Kochenberger, Abraham P. Punnen
2022· book-chapter· en· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Nonlinear equations
John C. Nash
2014· other· en· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Heuristics for generating additive spanners
Michael J. Letourneau
2004· dissertation· en· Summit (Simon Fraser University)· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
How to Compute a Local Minimum of the MPCC
Tangi Migot, Jean‐Pierre Dussault, Mounir Haddou, Abdesselam Kadrani
2017· article· en· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Orientation
S. Zlobec
2001· book-chapter· en· Applied optimization· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Linear Equalities in Blackbox Optimization
Charles Audet, Sébastien Le Digabel, Mathilde Peyrega
2014· article· en· PolyPublie (École Polytechnique de Montréal)· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
2004 Another Bad Year For Journalists
2005· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Erratum to: The approximation to a fixed point
Sibylla Prieß-Crampe, Paulo Ribenboim
2013· erratum· en· Journal of Fixed Point Theory and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Linear Programming
H. A. Eiselt, C.-L. Sandblom
2000· book-chapter· en· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Theory Embedded Learning
Cheng Chi
2023· dissertation· TSpace· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
General Properties of Algorithms
A. Antoniou, Wu-Sheng Lu
2021· book-chapter· en· Texts in computer science· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Local Polynomial $$L_p$$-norm Regression
Ladan Tazik, James E. Stafford, W. John Braun
2025· article· en· Statistics and Computing· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Least-Index Anticycling Rules
Tamás Terlaky
2001· book-chapter· en· Encyclopedia of Optimization· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Chapter 9: R&D Directions
Thomas Coleman, Xu Wei
2016· book-chapter· en· Society for Industrial and Applied Mathematics eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About