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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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Advanced Image and Video Retrieval 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.

1,044 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.
1,044 works in the cohort · of 4,299,418page 5 of 21

Labels cover 2 of 1,044 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 1,044 of 1,044 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
Kernel Latent SVM for Visual Recognition
Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
afffundunlabeled
Thesaurus
Amin Ghasemazar, Prashant J. Nair, Mieszko Lis
2020· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
21
citations
affno abstractunlabeled
Feature points for multisensor images
Sajid Saleem, Abdul Bais, Robert Sablatnig, Ayaz Ahmad, Noman Naseer
2017· article· en· Computers & Electrical Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Robust vehicle detection in low-resolution aerial imagery
Samir Sahli, Yueh Ouyang, Yunlong Sheng, Daniel A. Lavigne
2010· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Pixel-Wise Warping for Deep Image Stitching
Hyeokjun Kweon, Hyeonseong Kim, Yoonsu Kang, Young-Ho Yoon, Wooseong Jeong, Kuk‐Jin Yoon
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Scalable Video Coding for Humans and Machines
Hyomin Choi, Ivan V. Bajić
2022· article· en· 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affno abstractunlabeled
The Representation and Matching of Images Using Top Points
M. Fatih Demirci, Bram Platel, Ali Shokoufandeh, Luc Florack, Sven Dickinson
2009· article· en· Journal of Mathematical Imaging and Vision· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Learning to Rank System Configurations
Romain Deveaud, Josiane Mothe, Jian‐Yun Nie
2016· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Generating Classic Mosaics with Graph Cuts
Yu Liu, Olga Veksler, Olivier Juan
2010· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations

How this was built: Screen · Findings · About