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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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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
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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 4 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.

afffundno abstractunlabeled
Surrogate based MDO of a canard configuration aircraft
Tiago S. Jesus, Martin Sohst, José Vale, Afzal Suleman
2021· article· en· Structural and Multidisciplinary Optimization· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Discrete Event Simulation: State of the Art
Eduard Babulak, Ming Wang
2007· article· en· International Journal of Online and Biomedical Engineering (iJOE)· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
afffundunlabeled
Cell-DEVS for Social Phenomena Modeling
Hoda Khalil, Gabriel Wainer
2020· article· en· IEEE Transactions on Computational Social Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
DECOVALEX-2023: Task F Specification (Revision 10)
Tara LaForce, Richard Jayne, Rosemary Leone, Paul Mariner, Emily Stein, Son Nguyen +1 more
2023· report· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
afffundunlabeled
E-CARGO/RBC Research Guide: A Road Map for Researchers
Haibin Zhu, Dongning Liu, Hua Ma, Yin Sheng, Libo Zhang, Qian Jiang
2024· article· en· IEEE Systems Man and Cybernetics Magazine· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Activity in PythonPDEVS
Yentl Van Tendeloo, Hans Vangheluwe
2014· article· en· ITM Web of Conferences· Decision Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
The dependence list in time warp
Jing Lei Zhang, C. Tropper
2002· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Uncertainty on Discrete-Event System Simulation
Damián Vicino, Gabriel Wainer, Olivier Dalle
2021· article· en· ACM Transactions on Modeling and Computer Simulation· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Psst, Have I Got a Model for You . . .
J. Jaime
2014· editorial· en· Medical Decision Making· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Composite $T^2$ test for high-dimensional data
Long Feng, Changliang Zou, Zhaojun Wang, Lixing Zhu
2017· article· en· Statistica Sinica· Decision Sciences
machine prediction:candidate · noneconsensus · none
8
citations
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