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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.

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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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Scheduling and Timetabling Solutions
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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.

303 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.
303 works in the cohort · of 4,299,418page 4 of 7

Labels cover 1 of 303 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 303 of 303 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.

affunlabeled
Rostering for a Restaurant
Ármann Ingólfsson, Kwun Tong Jonathan Cheng
2002· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
A MinCumulative Resource Constraint
Yanick Ouellet, Claude-Guy Quimper
2022· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
The MIP Workshop 2023 Computational Competition on reoptimization
Suresh Bolusani, Mathieu Besançon, Ambros Gleixner, Timo Berthold, Claudia D’Ambrosio, Gonzalo Muñoz +2 more
2024· article· en· Mathematical Programming Computation· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
afffundno abstractunlabeled
Exact Coloring of Sparse Matrices
Shahadat Hossain, Ahamad Imtiaz Khan
2018· book-chapter· en· Springer proceedings in mathematics & statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affvenueunlabeled
INNOVATIVE COURSE SCHEDULING AND CURRICULUM DESIGN
Ali Akgündüz, Yong Zeng
2017· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Variable Space Search for Graph Coloring
Alain Hertz, Matthieu Plumettaz, Nicolas Zufferey
2006· article· fr· Archive ouverte UNIGE (University of Geneva)· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
New Algorithms for Graph Coloring Problem
Weidong Chen
2004· article· en· Microcomputer applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
A General Multi-Shift Scheduling System
Gilbert Laporte, Gilles Pesant
2001· article· en· Les Cahiers du GERAD· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
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