MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Advanced Vision and Imaging
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,705 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,705 works in the cohort · of 4,299,418page 5 of 35

Labels cover 1 of 1,705 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 1,705 of 1,705 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
Image editing in the contour domain
James H. Elder, R.M. Goldberg
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
afffundunlabeled
Perception-driven semi-structured boundary vectorization
Shayan Hoshyari, Edoardo Alberto Dominici, Alla Sheffer, Nathan Carr, Zhaowen Wang, Duygu Ceylan +1 more
2018· article· en· ACM Transactions on Graphics· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affno abstractunlabeled
Plenoptic Image Editing
Steven M. Seitz, Kiriakos N. Kutulakos
2002· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
fundno affunlabeled
Hardware‐Accelerated Rendering of Photo Hulls
Ming Li, Marcus Magnor, Hans‐Peter Seidel
2004· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
affno abstractunlabeled
On Learning Conditional Random Fields for Stereo
Christopher Pal, Jerod Weinman, Lam C. Tran, Daniel Scharstein
2010· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
Segmentation Framework Based on Label Field Fusion
Pierre‐Marc Jodoin, Max Mignotte, Christophe Rosenberger
2007· article· en· IEEE Transactions on Image Processing· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Techniques for automated reverse storyboarding
R.D. Dony, J.W. Mateer, John A. Robinson
2005· article· en· IEE Proceedings - Vision Image and Signal Processing· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
afffundunlabeled
Dense-disparity estimation from feature correspondences
Janusz Konrad, Zhong-Dan Lan
2000· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations

How this was built: Screen · Findings · About