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
Year range
→
Sort
Language
Type
Field
Venue
Topic
Gaussian Processes and Bayesian Inference
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.

309 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.
309 works in the cohort · of 4,299,418page 5 of 7

Labels cover 0 of 309 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 309 of 309 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Modeling Nonlinear Beta Probability Fields
2012· book-chapter· en· Quantitative geology and geostatistics· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
afffundunlabeled
The Bias in Bayes and How to Measure it
2012· article· en· Pakistan Journal of Statistics and Operation Research· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
1
citations
affunlabeled
Source data for PointNovo
2020· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+open_science+insufficient_payloadconsensus · open_science+insufficient_payload
1
citations
affno abstractunlabeled
Gaussian Process Reinforcement Learning
2014· book-chapter· en· Encyclopedia of Machine Learning and Data Mining· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
venueno affno abstractunlabeled
10.1145/3757377.3794993
2000· article· en· Time to knit· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Bayesian sparse factor regression trees
2018· dissertation· en· eScholarship@McGill (McGill)· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+insufficient_payloadconsensus · metaepi_narrow
0
citations
← previousnext →

How this was built: Screen · Findings · About