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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
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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.

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 8 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. 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
Efficient retrieval by shape content
Euripides G. M. Petrakis, Evangelos Milios
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Learning to recognize objects
Walter F. Bischof
2000· review· en· Spatial Vision· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Inducing features from visual noise
Andrew L. Cohen, Richard M. Shiffrin, Jason M. Gold, David A. Ross, Michael Ross
2007· article· en· Journal of Vision· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
QUANTIFYING NEARNESS IN VISUAL SPACES
Christopher J. Henry, Sheela Ramanna, Daniel Levy
2012· article· en· Cybernetics & Systems· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Discrete Representation of Top Points via Scale Space Tessellation
Bram Platel, M. Fatih Demirci, Ali Shokoufandeh, Luc Florack, Frans Kanters, Bart M. ter Haar Romeny +1 more
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Semantic-Based Cross-Media Image Retrieval
Ahmed Id Oumohmed, Max Mignotte, Jian‐Yun Nie
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
CW-SSIM kernel based random forest for image classification
Guangzhe Fan, Zhou Wang, Jiheng Wang
2010· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
A Collaborative Bayesian Image Annotation Framework
Rui Zhang, Kui Wu, Kim–Hui Yap, Ling Guan
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Clustering for Photo Retrieval at Image CLEF 2008
Diana Inkpen, Marc Stogaitis, François DeGuire, Muath Alzghool
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
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