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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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Model Reduction and Neural Networks
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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.

790 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.
790 works in the cohort · of 4,299,418page 1 of 16

Labels cover 0 of 790 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 790 of 790 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.

affunlabeled
Hamiltonian Neural Networks
Samuel Greydanus, Misko Dzamba, Jason Yosinski
2019· article· en· arXiv (Cornell University)· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
185
citations
affunlabeled
Subspace neural physics
Daniel Holden, Bang Chi Duong, Sayantan Datta, Derek Nowrouzezahrai
2019· article· en· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
78
citations
affunlabeled
A Review of Proper Modeling Techniques
Tulga Ersal, Hosam K. Fathy, Geoff Rideout, Loucas S. Louca, Jeffrey L. Stein
2008· review· en· Journal of Dynamic Systems Measurement and Control· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
68
citations
afffundno abstractunlabeled
Multibody dynamics and control using machine learning
Arash Hashemi, Grzegorz Orzechowski, Aki Mikkola, John McPhee
2023· article· en· Multibody System Dynamics· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
53
citations
fundno affunlabeled
A review of mechanistic learning in mathematical oncology
John Metzcar, Catherine R. Jutzeler, Paul Macklin, Alvaro Köhn‐Luque, Sarah C. Brüningk
2024· review· en· Frontiers in Immunology· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
51
citations
affunlabeled
Publishing unbinned differential cross section results
M. Arratia, Anja Butter, M. Campanelli, V. Croft, Dag Gillberg, Aishik Ghosh +7 more
2022· article· en· Journal of Instrumentation· Physics and Astronomy
machine prediction:candidate · metaresearchconsensus · none
46
citations
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