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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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Optimization and Packing Problems
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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.

349 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.
349 works in the cohort · of 4,299,418page 3 of 7

Labels cover 0 of 349 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 349 of 349 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
Bound constraints for Point Packing in a Square
Alberto Costa, Pierre Hansen, Leo Liberti
2011· article· en· Cologne Twente Workshop on Graphs and Combinatorial Optimization· Engineering
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Evolving Point Packings in the Plane
Daniel Ashlock, Philip Hingston, Cameron McGuinness
2015· book-chapter· en· Lecture notes in computer science· Engineering
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Dyadic Packing of Dijoins
Bertrand Guenin
2025· article· es· SIAM Journal on Discrete Mathematics· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Chains-into-bins processes
Tuğkan Batu, Petra Berenbrink, Colin Cooper
2011· article· en· Journal of Discrete Algorithms· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Split cuts from sparse disjunctions
Ricardo Fukasawa, Laurent Poirrier, Shenghao Yang
2020· article· en· Mathematical Programming Computation· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
Worst-case analysis for new online bin packing problems
Mauro Maria Baldi, Teodor Gabriel Crainic, Guido Perboli, Roberto Tadei
2013· article· en· PORTO Publications Open Repository TOrino (Politecnico di Torino)· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
A Novel Method to Solve Neural Knapsack Problems
Duanshun Li, Dong‐Eun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, Noseong Park
2021· article· en· International Conference on Machine Learning· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
PAAD: Panelization algorithm for architectural designs
Andrew Fisher, Xing Tan, A. H. M. Muntasir Billah, Pawan Lingras, Jimmy Xiangji Huang, Vijay Mago
2024· article· en· PLoS ONE· Engineering
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About