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
Advanced Statistical Methods and Models
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.

1,193 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.
1,193 works in the cohort · of 4,299,418page 16 of 24

Labels cover 6 of 1,193 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 1,193 of 1,193 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.

afffundaboutunlabeled
A Conversation with Martin Bradbury Wilk
Christian Genest, Gordon Brackstone
2010· article· en· Statistical Science· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
afffundaboutunlabeled
A Conversation with James O. Ramsay
Christian Genest, Johanna Nešlehová
2014· article· en· International Statistical Review· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
afffundvenueunlabeled
Best monotone M‐estimators
Adam W. Kolkiewicz
2003· article· en· Canadian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Gini-PLS Regressions
Stéphane Mussard, Fattouma Souissi-Benrejab
2018· article· en· Journal of Quantitative Economics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Errors in Variables
Janet Raboud
2014· other· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Logarithmic Regression
A El-Shaarawi, R. Viveros
2014· other· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
My Chancy Life as a Statistician
J. N. K. Rao
2018· article· en· International Statistical Review· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Variables and Constraints
Charles Audet, Warren Hare
2017· book-chapter· en· Springer series in operations research/Springer series in operations research and financial engineering· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
← previousnext →

How this was built: Screen · Findings · About