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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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npj Computational Materials
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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.

41 results · 1 filter active ·
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20172025
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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.
41 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 41 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 41 of 41 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.

afffundunlabeled
Hyperactive learning for data-driven interatomic potentials
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Cśanyi
2023· article· en· npj Computational Materials· Materials Science
machine prediction:candidate · noneconsensus · none
102
citations
afffundunlabeled
Interpretable discovery of semiconductors with machine learning
Hitarth Choubisa, Petar Todorović́, João M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy +2 more
2023· article· en· npj Computational Materials· Materials Science
machine prediction:candidate · noneconsensus · none
39
citations
afffundunlabeled
Quantifying defects in thin films using machine vision
Nina Taherimakhsousi, Benjamin P. MacLeod, Fraser G. L. Parlane, Thomas D. Morrissey, Edward P. Booker, Kevan E. Dettelbach +1 more
2020· preprint· en· npj Computational Materials· Engineering
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About