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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.

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 7 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.

affno abstractunlabeled
Randomization Methods in Optimization and Adaptive Control
László Gerencsér, Zsuzsanna Vágó, Håkan Hjalmarsson
2007· book-chapter· en· Lecture notes in control and information sciences· Decision Sciences
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
ANML: A language for describing networks
Cameron Kiddle, Rob Simmonds, David K. Wilson, Brian Unger
2002· article· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
5
citations
venueno affunlabeled
A PEER REVIEWED ONLINE COMPUTATIONAL MODELING FRAMEWORK
Pieter J. Mosterman, Don Bouldin, Andrzej Ruciński
2011· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Decision Sciences
distilled prediction:candidate · metaresearchconsensus · none
4
citations
affunlabeled
Lessons from a conceptual modeling exercise
Margaret L. Loper, Louis G. Birta, Gilbert Arbez
2012· article· en· Winter Simulation Conference· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
4
citations
affno abstractunlabeled
History of Simulation
Bernard P. Zeigler, Breno Bernard Nicolau de França, Valdemar Vicente Graciano Neto, Raymond R. Hill, L. Champagne, Tuncer Ören
2023· book-chapter· en· Simulation foundations, methods and applications· Decision Sciences
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
4
citations
affunlabeled
Monte Carlo Simulation
J.F. Hayes, Thimma V. J. Ganesh Babu
2004· other· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
4
citations
affno abstractunlabeled
Advances in Modeling and Simulation
Zdravko I. Botev, Alexander Keller, Christiane Lemieux, Bruno Tuffin
2022· book· en· Decision Sciences
distilled prediction:candidate · insufficient_payloadconsensus · none
4
citations
affunlabeled
Simulation by example for complex systems
Amir Kalbasi, Diwakar Krishnamurthy, Jerry Rolia, Sharad Singhal
2014· article· en· Winter Simulation Conference· Decision Sciences
distilled prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About