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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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Machine Learning in Materials Science
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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.

1,108 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.
1,108 works in the cohort · of 4,299,418page 5 of 23

Labels cover 4 of 1,108 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 1,108 of 1,108 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.

fundno affunlabeled
A machine learning route between band mapping and band structure
R. Patrick Xian, Vincent Stimper, Marios Zacharias, Maciej Dendzik, Shuo Dong, Samuel Beaulieu +6 more
2022· article· en· Nature Computational Science· Materials Science
machine prediction:candidate · noneconsensus · none
26
citations
afffundgemma · no categorygpt · no categorymodels agree
A benchmark dataset for Hydrogen Combustion
Xingyi Guan, Akshaya Kumar Das, Christopher J. Stein, Farnaz Heidar‐Zadeh, Luke W. Bertels, Meili Liu +7 more
2022· article· en· Scientific Data· Materials Science
machine prediction:candidate · noneconsensus · none
26
citations
afffundunlabeled
Atlas: A Brain for Self-driving Laboratories
Riley J. Hickman, Malcolm Sim, Sergio Pablo‐García, Ivan Woolhouse, Han Hao, Zeqing Bao +4 more
2023· preprint· en· ChemRxiv· Materials Science
machine prediction:candidate · noneconsensus · none
24
citations
fundno affunlabeled
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey +25 more
2022· preprint· en· White Rose Research Online (University of Leeds, The University of Sheffield, University of York)· Materials Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Correction: QSAR without borders
Eugene Muratov, Jürgen Bajorath, Robert P. Sheridan, Igor V. Tetko, Dmitry Filimonov, Vladimir Poroikov +13 more
2020· review· en· Chemical Society Reviews· Materials Science
machine prediction:candidate · noneconsensus · none
22
citations
afffundno abstractunlabeled
Electronic Stress as a Guiding Force for Chemical Bonding
Alfredo Guevara‐García, Paul W. Ayers, Samantha Jenkins, Steven R. Kirk, Eleonora Echegaray, Alejandro Toro‐Labbé
2011· article· en· Topics in current chemistry· Materials Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Roadmap on data-centric materials science
Peter Benner, Tristan Bereau, Volker Blüm, Mario Boley, Christian Carbogno, C. Richard A. Catlow +54 more
2024· article· en· Modelling and Simulation in Materials Science and Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
20
citations

How this was built: Screen · Findings · About