MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Neural Networks and 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.

2,372 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.
2,372 works in the cohort · of 4,299,418page 24 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 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
Overcoming sensory-memory interference in working memory circuits
Andrii Zahorodnii, Diego Mendoza-Halliday, Julio Martínez-Trujillo, Ning Qian, Robert Desimone, Christopher J. Cueva
2025· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Modeling Slowly Changing Dimensions in ORM
Ron McFadyen
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Probability Learning by Perceptrons and People
Michael R. W. Dawson
2022· article· en· Comparative Cognition & Behavior Reviews· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Neural Spacetimes for DAG Representation Learning
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein
2024· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Perceptrons
Ke-Lin Du, M. N. S. Swamy
2019· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
On Learnability wih Computable Learners.
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
2020· article· en· Algorithmic Learning Theory· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
The dynamics inside the box
John E. Lewis
2000· article· en· Nature Neuroscience· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Nonparametric Statistical Tests
David L. Streiner
2010· other· en· The Corsini Encyclopedia of Psychology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
BALANCED FUZZY COMPUTING UNIT
Władysław Homenda, Witold Pedrycz
2005· article· en· International Journal of Uncertainty Fuzziness and Knowledge-Based Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
← previousnext →

How this was built: Screen · Findings · About