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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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Computer Vision and Image Understanding
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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.

91 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.
91 works in the cohort · of 4,299,418page 2 of 2

Labels cover 0 of 91 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 91 of 91 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.

affno abstractunlabeled
Locality regularized group sparse coding for action recognition
Mohammad Ali Bagheri, Qigang Gao, Sérgio Escalera, Thomas B. Moeslund, Huamin Ren, Elham Etemad
2017· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Anytime similarity measures for faster alignment
Rupert Brooks, Tal Arbel, Doina Precup
2008· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Structure-aware feature stylization for domain generalization
Milad Cheraghalikhani, Mehrdad Noori, David Osowiechi, Gustavo A. Vargas Hakim, Ismail Ben Ayed, Christian Desrosiers
2024· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
A survey on RGB-D datasets
Hélio Pedrini
2022· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Towards 4D human video stylization
Xinxin Zuo, Fangzhou Mu, Jian Wang, Ming–Hsuan Yang
2025· article· en· Computer Vision and Image Understanding· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Open cross-domain visual search
William Thong, Pascal Mettes, Cees G. M. Snoek
2020· preprint· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About