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
Visual Attention and Saliency Detection
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.

509 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.
509 works in the cohort · of 4,299,418page 8 of 11

Labels cover 1 of 509 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 509 of 509 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Visual Prompting via Partial Optimal Transport
Mengyu Zheng, Zhiwei Hao, Yehui Tang, Chang Xu
2024· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
afffundno abstractunlabeled
Attention to complex scene features
Gaeun Son, Michael L. Mack, Dirk B. Walther
2025· article· en· Attention Perception & Psychophysics· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
1
citations
affunlabeled
Adaptive semantic Bayesian framework for image attention
Wei Zhang, Q. M. Jonathan Wu, Guanghui Wang
2008· article· en· Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
1
citations
aboutno affunlabeled
Saliency Detection from Subitizing Processing
Carola Figueroa-Flores
2023· book-chapter· en· IntechOpen eBooks· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
1
citations
affunlabeled
Priming Neural Networks
Amir Rosenfeld, Mahdi Biparva, John K. Tsotsos
2018· preprint· en· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Inter-Observer Visual Congruency in Video-Viewing
Jiaomin Yue, Qiang Lu, Dandan Zhu, Xiongkuo Min, Xiao–Ping Zhang, Guangtao Zhai
2021· article· en· 2021 International Conference on Visual Communications and Image Processing (VCIP)· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
1
citations
affunlabeled
RAIC: Robust Adaptive Image Clustering
Antoine Leblond, Claude Kauffmann
2018· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
fundno affunlabeled
Leveraging rapid scene perception in attentional learning
Juliana Daphne Adema, Shuran Tang, Nahal Alizadeh Saghati, Michael L. Mack
2021· article· en· eScholarship (California Digital Library)· Computer Science
distilled prediction:candidate · scholarly_communication+insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About