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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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Simulation Techniques 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.

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.

889 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.
889 works in the cohort · of 4,299,418page 5 of 18

Labels cover 2 of 889 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 889 of 889 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Multiscale representation of simulated time
Rhys Goldstein, Azam Khan, Olivier Dalle, Gabriel Wainer
2017· article· en· SIMULATION· Decision Sciences
distilled prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Complex System Engineering Simulation through Co-Simulation
Sylvain Pagerit, Thierry Roudier, P. Sharer, Aymeric Rousseau
2014· article· en· SAE technical papers on CD-ROM/SAE technical paper series· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
8
citations
affno abstractunlabeled
Improved Cell-DEVS Models for Fire Spreading Analysis
Matthew R. MacLeod, Rachid Chreyh, Gabriel Wainer
2006· book-chapter· en· Lecture notes in computer science· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
8
citations
venueno affunlabeled
Small area estimators in a simulation test
Annika Kangas, Mari Myllymäki, Petteri Packalén
2024· article· en· Canadian Journal of Forest Research· Decision Sciences
distilled prediction:candidate · noneconsensus · none
7
citations
aboutno affunlabeled
Scale-up of Reactive Flow Through Network Flow Modeling
Daesang Kim
2008· dissertation· en· SUNY Digital Repository Support (State University of New York System)· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
affunlabeled
Unbiased metamodeling via likelihood ratios
Jing Dong, M. Ben Feng, Barry L. Nelson
2018· article· en· Winter Simulation Conference· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
7
citations
affunlabeled
A flow injection model using Cell-DEVS
Alejandro Troccoli, J. Ameghino, Fernando A. Iñón, Gabriel Wainer
2003· article· en· Decision Sciences
distilled prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Defining models of urban traffic using the TSC tool
Mariana Lo Tártaro, César O. Torres, Gabriel Wainer
2002· article· en· Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304)· Decision Sciences
distilled prediction:candidate · noneconsensus · none
6
citations
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