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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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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.

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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
Evidence
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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 1 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. 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
A Tutorial on the Cross-Entropy Method
Pieter-Tjerk de Boer, Dirk P. Kroese, Shie Mannor, Reuven Y. Rubinstein
2005· article· en· Annals of Operations Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
3,031
citations
affunlabeled
Managing the Unexpected
Kathleen M. Sutcliffe, Marlys K. Christianson
2011· book· en· Oxford University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
688
citations
affno abstractunlabeled
Modelling and Simulation
Louis G. Birta, Gilbert Arbez
2019· book· en· Simulation foundations, methods and applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
631
citations
affunlabeled
Co-Simulation
Cláudio Gomes, Casper Thule, David Broman, Peter Gorm Larsen, Hans Vangheluwe
2018· review· en· ACM Computing Surveys· Decision Sciences
machine prediction:candidate · noneconsensus · none
355
citations
affunlabeled
CD++: a toolkit to develop DEVS models
Gabriel Wainer
2002· article· en· Software Practice and Experience· Decision Sciences
machine prediction:candidate · noneconsensus · none
256
citations
affunlabeled
An evaluation of DEVS simulation tools
Yentl Van Tendeloo, Hans Vangheluwe
2016· article· en· SIMULATION· Decision Sciences
machine prediction:candidate · noneconsensus · none
71
citations
affno abstractunlabeled
N-dimensional Cell-DEVS Models
Gabriel Wainer, Norbert Giambiasi
2002· article· en· Discrete Event Dynamic Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
68
citations
affno abstractunlabeled
Modelling and Simulation
Louis G. Birta, Gilbert Arbez
2013· book· en· Simulation foundations, methods and applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
54
citations
affunlabeled
Building Devs Models with the Cadmium Tool
Laouen Belloli, Damián Vicino, Cristina Ruiz-Martín, Gabriel Wainer
2019· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
47
citations
affunlabeled
Simulation in Java with SSJ
Pierre L’Ecuyer, Éric Buist
2006· article· en· Proceedings of the Winter Simulation Conference, 2005.· Decision Sciences
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
Implementing parallel Cell-DEVS
Alejandro Troccoli, Gabriel Wainer
2003· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
39
citations
venueno affno abstractunlabeled
A discrete flow model for dynamic network loading
Michael Mahut
2000· article· en· Library and Archives Canada (Government of Canada)· Decision Sciences
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Meta-Models are models too
Hans Vangheluwe, Juan de Lara
2003· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
32
citations
affunlabeled
SIMULATION AND REALITY: THE BIG PICTURE
Tuncer Ören
2010· article· en· Advances in Complex Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
31
citations
affunlabeled
Effective real-time simulations of event-based systems
C.A. Rabbath, M. Abdoune, J. Bélanger
2002· article· en· 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165)· Decision Sciences
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Body of Knowledge for Modeling and Simulation
Tuncer Ören, Bernard P. Zeigler, Andreas Tolk
2023· book· en· Simulation foundations, methods and applications· Decision Sciences
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Debugging Parallel DEVS
Simon Van Mierlo, Yentl Van Tendeloo, Hans Vangheluwe
2016· article· en· SIMULATION· Decision Sciences
machine prediction:candidate · noneconsensus · none
26
citations

How this was built: Screen · Findings · About