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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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Computational Optimization and Applications
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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.

73 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.
73 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 73 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 73 of 73 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
On handling indicator constraints in mixed integer programming
Pietro Belotti, Pierre Bonami, Matteo Fischetti, Andrea Lodi, Michele Monaci, Amaya Nogales-Gómez +1 more
2016· article· en· Computational Optimization and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
The generalized trust region subproblem
Ting Kei Pong, Henry Wolkowicz
2014· article· en· Computational Optimization and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
66
citations
affno abstractunlabeled
Portfolio Selection and Transactions Costs
Michael J. Best, Jaroslava Hlouskova
2003· article· en· Computational Optimization and Applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Optimizing Preventive Maintenance Models
M. C. Bartholomew‐Biggs, Bruce Christianson, Ming J. Zuo
2006· article· en· Computational Optimization and Applications· Engineering
machine prediction:candidate · noneconsensus · none
42
citations
affno abstractunlabeled
Clustering via minimum volume ellipsoids
Romy Shioda, Levent Tunçel
2007· article· en· Computational Optimization and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
23
citations
afffundno abstractunlabeled
Linear equalities in blackbox optimization
Charles Audet, Sébastien Le Digabel, Mathilde Peyrega
2014· article· en· Computational Optimization and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Mixed-logit network pricing
François Gilbert, Patrice Marcotte, Gilles Savard
2013· article· en· Computational Optimization and Applications· Social Sciences
machine prediction:candidate · noneconsensus · none
20
citations
afffundno abstractunlabeled
A parameterized Douglas–Rachford algorithm
Dongying Wang, Xianfu Wang
2019· article· en· Computational Optimization and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations

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