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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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Industrial Vision Systems and Defect Detection
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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.

743 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.
743 works in the cohort · of 4,299,418page 10 of 15

Labels cover 0 of 743 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 743 of 743 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Deep SVBRDF Estimation on Real Materials
Louis-Philippe Asselin, Denis Laurendeau, Jean‐François Lalonde
2020· preprint· en· Engineering
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
fundno affunlabeled
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
2024· preprint· en· arXiv (Cornell University)· Engineering
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Defectors: A Large Scale Python Dataset for Defect Prediction
Parvez Mahbub, Ohiduzzaman Shuvo, Mohammad Masudur Rahman
2023· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Engineering
distilled prediction:candidate · metaepi_narrow+sts+insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Defectors: A Large Scale Python Dataset for Defect Prediction
Parvez Mahbub, Ohiduzzaman Shuvo, Mohammad Masudur Rahman
2023· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Engineering
distilled prediction:candidate · metaepi_narrow+sts+insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affunlabeled
Visual Task Classification using Classic Machine Learning and CNNs
Devangi Vilas Chinchankarame, Noha Elfiky, Nada Attar
2022· article· en· Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science· Engineering
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
EVALUATING ATRYPIDE BRACHIOPOD FEEDING USING 3D PRINTED MODELS
Rylan V. Dievert, Kristina M. Barclay, Darrin J. Molinaro, Lindsey R. Leighton
2018· article· en· Abstracts with programs - Geological Society of America· Engineering
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction
Hossein Keshavarz, Meiyappan Nagappan
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Engineering
distilled prediction:candidate · metaepi_narrow+sts+insufficient_payloadconsensus · insufficient_payload
0
citations
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How this was built: Screen · Findings · About