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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.

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.

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 8 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.

affvenueunlabeled
Experimental methods in chemical engineering: Monte Carlo
Ergys Pahija, Soonho Hwangbo, Thomas Saulnier‐Bellemare, Gregory S. Patience
2024· article· en· The Canadian Journal of Chemical Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
11
citations
afffundunlabeled
Data-science driven autonomous process optimization
Melodie Christensen, Lars P. E. Yunker, Folarin Adedeji, Florian Häse, Loı̈c M. Roch, Tobias Gensch +5 more
2020· preprint· en· ChemRxiv· Materials Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Proof-of-work consensus by quantum sampling
Deepesh Singh, Gopikrishnan Muraleedharan, Boxiang Fu, Chen-Mou Cheng, Nicolas Roussy Newton, Peter P. Rohde +1 more
2025· article· en· Quantum Science and Technology· Materials Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Exploiting Machine Learning in Multiscale Modelling of Materials
G. Anand, Swarnava Ghosh, Liwei Zhang, Angesh Anupam, Colin L. Freeman, Christoph Ortner +2 more
2022· article· en· Journal of The Institution of Engineers (India) Series D· Materials Science
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Prose 3: Early Ischemia Detection Using Digitized ST Values
Bilal Haider, Homer Yang, Hassan Koroshi Talab, Alan Chaput, Terrence D. Ruddy, James M. Watters +3 more
2011· article· en· CMBES Proceedings· Materials Science
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About