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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
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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.

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 8 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
Preface: Linear optimization
Antoine Deza, Frédéric Meunier
2018· article· en· Discrete Applied Mathematics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Volume and variance in the linear statistical model
Isabel Cristina Da Silva Araújo, M.P. de Oliveira
2002· article· en· Linear Algebra and its Applications· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Modeling and solving bundle adjustment problems
Célestine Angla, Jean Bigeon, Dominique Orban
2020· preprint· en· PolyPublie (École Polytechnique de Montréal)· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Introduction to Stability
S. Zlobec
2001· book-chapter· en· Applied optimization· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
The cosine measure relative to a subspace
Charles Audet, Warren Hare, Gabriel Jarry–Bolduc
2025· article· en· Computational Optimization and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Quasi-Newton Methods
A. Antoniou, Wu-Sheng Lu
2021· book-chapter· en· Texts in computer science· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Improved asymptotic analysis for SUMT methods
Jean‐Pierre Dussault
2011· article· en· Federated Conference on Computer Science and Information Systems· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Criss-Cross Pivoting Rules
Tamás Terlaky
2001· book-chapter· en· Encyclopedia of Optimization· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
On self-regular IPMs
Maziar Salahi, Renata Sotirov, Tamás Terlaky
2004· article· en· Top· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About