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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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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 3 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.

affno abstractunlabeled
Learning to Recognize Objects with Little Supervision
Peter Carbonetto, Gyuri Dorkó, Cordelia Schmid, Hendrik Kück, Nando de Freitas
2007· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
47
citations
affunlabeled
Canonical Skeletons for Shape Matching
Matthijs van Eede, Diego Macrini, Alexandru Telea, Cristian Sminchisescu, Sara Dickinson
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
47
citations
affunlabeled
Image-guided maze construction
Jie Xu, Craig S. Kaplan
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Sparse Flexible Models of Local Features
Gustavo Carneiro, David Lowe
2006· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
Categorization of natural scenes
Julia Vogel, Adrian Schwaninger, Christian Wallraven, HH Bülthoff
2007· article· en· ACM Transactions on Applied Perception· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affno abstractunlabeled
Bone graphs: Medial shape parsing and abstraction
Diego Macrini, Sven Dickinson, David J. Fleet, Kaleem Siddiqi
2011· article· en· Computer Vision and Image Understanding· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
afffundno abstractunlabeled
Gentropy: evolving 2D textures
Andrea L. Wiens, Brian J. Ross
2002· article· en· Computers & Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affno abstractunlabeled
Spatially Local Coding for Object Recognition
Sancho McCann, David Lowe
2013· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
afffundunlabeled
A dataset of labelled objects on raw video sequences
Hyomin Choi, Elahe Hosseini, Saeed Ranjbar Alvar, Robert Cohen, Ivan V. Bajić
2020· article· en· Data in Brief· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
affunlabeled
Order-Preserving Moves for Graph-Cut-Based Optimization
Xiaoqing Liu, Olga Veksler, Jagath Samarabandu
2009· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
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