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
PolyPublie (École Polytechnique de Montréal)
Topic
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.

2,428 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.
2,428 works in the cohort · of 4,299,418page 9 of 49

Labels cover 10 of 2,428 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 2,428 of 2,428 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
Sample-based approximate regularization
Philip Bachman, Amir‐massoud Farahmand, Doina Precup
2014· article· en· PolyPublie (École Polytechnique de Montréal)· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
A DDMRP implementation user feedbacks and stakes analysis
Guillaume Dessevre, Jacques Lamothe, Vincent Pomponne, Pierre Baptiste, Matthieu Lauras, Robert Pellerin
2020· preprint· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affno abstractunlabeled
Longitudinal Analysis of Bikesharing Usage in Montreal, Canada
Catherine Morency, Martin Trépanier, Alexis Frappier, Jean-Simon Bourdeau
2017· article· en· PolyPublie (École Polytechnique de Montréal)· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Modeling and solving bundle adjustment problems
Célestine Angla, Jean Bigeon, Dominique Orban
2020· preprint· en· PolyPublie (École Polytechnique de Montréal)· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
An exploratory study of the impact of software changeability
Foutse Khomh, Massimiliano Di Penta, Yann‐Gaël Guéhéneuc, Giuliano Antoniol
2009· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Alimentation traction du métro de Montréal
P.Y. Bertin
2004· article· fr· PolyPublie (École Polytechnique de Montréal)· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Optimization of Algorithms with OPAL
Charles Audet, Kien-Cong Dang, Dominique Orban
2012· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About