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 37 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
Fast computation of latent correlations
Grace Yoon, Christian L. Müller, Irina Gaynanova
2021· dataset· en· Figshare· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Cobre (for machine learning)
Christian Dansereau
2015· dataset· en· Figshare· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Semi-implicit back propagation
Ren Ping Liu, Xiaoqun Zhang
2023· article· en· Journal of Nonlinear and Variational Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Maximum tolerance and maximum greatest tolerance
2009· article· en· UPCommons institutional repository (Universitat Politècnica de Catalunya)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Autoregressive Typical Thermal States
Tarun Kumar, Leon Balents, Timothy H. Hsieh, Roger G. Melko
2025· preprint· en· ArXiv.org· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Computing EFMs consistent with equilibrium constants
Sabine Pérès, Philippe Dague, Mario Jolicœur, Stefan Schuster
2017· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
CODING DATA
Matthew Nelder
2019· dataset· en· Figshare· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Affine Feedforward Stochastic (AFS) Neural Network
Christian Gouriéroux, Alain Monfort
2025· other· en· Toulouse Capitole Publications (University Toulouse 1 Capitole)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
prepare-commit-msg.sample
Lucas Leemann, Leonardo Baccini
2020· dataset· en· Harvard Dataverse· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Lighter: Configuration-Driven Deep Learning
Ibrahim Hadžić, Suraj Pai, Keno K. Bressem, Borek Foldyna, Hugo J.W.L. Aerts
2025· article· en· The Journal of Open Source Software· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
A maximum entropy criterion for feature extraction
Donggao Deng, Daren Huang, Rong-Qing Jia, Wei Lin, Jianzhong Wang
2002· book-chapter· en· AMS/IP studies in advanced mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1145/3757377.3794966
AI Generated
2000· article· en· Time to knit· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
← previousnext →

How this was built: Screen · Findings · About