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
Multimodal Machine Learning Applications
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.

476 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.
476 works in the cohort · of 4,299,418page 3 of 10

Labels cover 3 of 476 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 476 of 476 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
Why can't José read?
Peter Carbonetto, Nando de Freitas
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
afffundunlabeled
VirtualHome: Simulating Household Activities via Programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler +1 more
2018· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
FigureQA: An Annotated Figure Dataset for Visual Reasoning
Samira Ebrahimi Kahou, Adam Atkinson, Vincent Michalski, Ákos Kádár, Adam Trischler, Yoshua Bengio
2017· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Learnable Pooling Methods for Video Classification
Sebastian Kmiec, Juhan Bae, Ruijian An
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Generative Multi-hop Retrieval
Hyunji Lee, Sohee Yang, Hanseok Oh, Minjoon Seo
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Large multimodal models evaluation: a survey
Zicheng Zhang, Junying Wang, Farong Wen, Yijin Guo, Xiangyu Zhao, Xinyu Fang +38 more
2025· article· en· Science China Information Sciences· Computer Science
machine prediction:candidate · metaresearchconsensus · none
10
citations

How this was built: Screen · Findings · About