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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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Modelling and Simulation in Materials Science and Engineering
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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.

54 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.
54 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 54 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 54 of 54 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
Roadmap on multiscale materials modeling
E. van der Giessen, Peter A. Schultz, Nicolas Bertin, Vasily V. Bulatov, Wei Cai, Gábor Csányi +14 more
2020· article· en· Modelling and Simulation in Materials Science and Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
203
citations
aboutno affunlabeled
The effect of cold spray impact velocity on deposit hardness
Victor K. Champagne, D.J. Helfritch, Matthew D. Trexler, Brian Gabriel
2010· article· en· Modelling and Simulation in Materials Science and Engineering· Engineering
machine prediction:candidate · noneconsensus · none
91
citations
affunlabeled
Atomistic modeling of pure Li and Mg–Li system
Young‐Min Kim, In‐Ho Jung, Byeong‐Joo Lee
2012· article· en· Modelling and Simulation in Materials Science and Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
66
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
affunlabeled
Benchmarking machine learning strategies for phase-field problems
Rémi Dingreville, Andreas E Roberston, Vahid Attari, Michael Greenwood, Nana Ofori-Opoku, Mythreyi Ramesh +2 more
2024· article· en· Modelling and Simulation in Materials Science and Engineering· Engineering
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Ordering of carbon in highly supersaturated <i>α</i> -Fe
Osamu Waseda, Julien Morthomas, Fabienne Ribeiro, Patrice Chantrenne, Chad W. Sinclair, Michel Perez
2018· article· en· Modelling and Simulation in Materials Science and Engineering· Engineering
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Carbon diffusion in concentrated Fe–C glasses
Siavash Soltani, Jörg Rottler, Chad W. Sinclair
2021· article· en· Modelling and Simulation in Materials Science and Engineering· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
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