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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
fundfunder
venuejournal
aboutaboutness

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 22 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

aboutno affunlabeled
Nanaimo Free Press [Monday, February 17, 1896]
2019· other· en· VIURRSpace (Vancouver Island University)· Materials Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
xwi’xwi’em’:
2016· other· en· UVic’s Research and Learning Repository (University of Victoria)· Materials Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Accelerating Materials Discovery with Machine Learning
2024· dissertation· en· Trinity's Access to Research Output (TARA) (Trinity College Dublin)· Materials Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+open_science+research_integrity+insufficient_payloadconsensus · metaepi_narrow+insufficient_payload
0
citations
fundno affno abstractunlabeled
Advances in intelligent epitaxy of semiconductor materials
Chao Shen, Kang Yang, Wenkang Zhan, Bo-Wei Xu, Zhaonan Li, Shujie Pan +3 more
2025· article· en· Progress in Quantum Electronics· Materials Science
distilled prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1145/3772318.3808882
AI Generated
2000· article· en· Time to knit· Materials Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
NLPModelsKnitro.jl: A thin KNITRO wrapper for NLPModels
2025· other· en· Zenodo (CERN European Organization for Nuclear Research)· Materials Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Code and Data for "Routescore: Punching the Ticket to More Efficient Materials Development"
Martin Seifrid, Riley J. Hickman, Andrés Aguilar‐Granda, Cyrille Lavigne, Jenya Vestfrid, Tony Wu +3 more
2021· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Materials Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+open_science+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
ISMRM Reproducible Research Study Group: Data for the paper "CG-SENSE revisited: Results from the first ISMRM reproducibility challenge"
Oliver Maier, Steven H. Baete, Alexander Fyrdahl, Kerstin Hammernik, Seb Harrevelt, Lars Kasper +8 more
2020· dataset· en· PolyPublie (École Polytechnique de Montréal)· Materials Science
distilled prediction:candidate · metaresearch+metaepi_narrow+sts+scholarly_communication+open_science+research_integrityconsensus · metaresearch+open_science
0
citations
aboutno affunlabeled
Why Don't We Quit?
2025· other· en· Goldsmiths (University of London)· Materials Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
0
citations
afffundno abstractunlabeled
Machine learning assisted canonical sampling (Mlacs)
Aloïs Castellano, Romuald Béjaud, Olivier Nadeau, Grégory Geneste, Gabriel Antonius, J. Bouchet +3 more
2025· preprint· en· Computer Physics Communications· Materials Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · open_science
0
citations
affno abstractunlabeled
SPCG Computational Artifact for SC25
2025· other· en· Zenodo (CERN European Organization for Nuclear Research)· Materials Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Maria Vinokurova
2021· other· en· OpenEdition (OpenEdition)· Materials Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affno abstractunlabeled
FOQ Canada
2005· article· en· Érudit (Université de Montréal)· Materials Science
distilled prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About