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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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Image Retrieval and Classification Techniques
Retraction
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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.

1,015 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.
1,015 works in the cohort · of 4,299,418page 10 of 21

Labels cover 2 of 1,015 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 1,015 of 1,015 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Communicating Image Content
Lisa Tang, Jenny Carter
2011· article· en· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Distinctive parts for shape classification
Chunyuan Li, Xinge You, A. Ben Hamza, Wu Zeng, Long Zhou
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Neighbourhoods, Classes and Near Sets
Christopher J. Henry
2011· article· en· WinnSpace (University of Winnipeg)· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
MPEG Video Coding: MPEG-1, 2, 4, and 7
Ze-Nian Li, Mark S. Drew, Jiangchuan Liu
2021· book-chapter· en· Texts in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
5
citations
affunlabeled
Feature fusion for image texture segmentation
D.A. Clausi, Huawu Deng
2004· article· en· Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Two case studies of very long-term retention
Ashleigh M. Maxcey, Richard M. Shiffrin, Denis Cousineau, Richard C. Atkinson
2021· article· en· Psychonomic Bulletin & Review· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Content-based Image Retrieval (CBIR)
University Research Chair
2009· book-chapter· en· Encyclopedia of Database Systems· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
citations
affunlabeled
Artificial intelligence in 2027
Maria Gini, Noa Agmon, Fausto Giunchiglia, Sven Koenig, Kevin Leyton‐Brown
2018· article· en· AI Matters· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Online Variational Learning for Medical Image Data Clustering
Meeta Kalra, Michael Osadebey, Nizar Bouguila, Marius Pedersen, Wentao Fan
2019· book-chapter· en· Unsupervised and semi-supervised learning· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
citations
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