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
Topic
Advanced Text Analysis Techniques
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.

552 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.
552 works in the cohort · of 4,299,418page 5 of 12

Labels cover 2 of 552 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 552 of 552 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.

affno abstractunlabeled
Inductive Reasoning and Chance Discovery
Ahmed Y. Tawfik
2004· article· en· Minds and Machines· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
venueno affunlabeled
PRIS at Knowledge Base Population 2013
Yan Li, Yichang Zhang, Doyu Li, Xin Tong, Jianlong Wang, Naiche Zuo +4 more
2013· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Can Human Assistance Improve a Computational Poet
Carolyn Lamb, Daniel G. Brown, Charles L. A. Clarke
2015· article· en· Proceedings of Bridges 2015: Mathematics, Music, Art, Architecture, Culture· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
TREC 2020 Podcasts Track Overview.
Rosie Jones, Ben Carterette, Ann Clifton, Jussi Karlgren, Aasish Pappu, Sravana Reddy +3 more
2020· article· en· Text REtrieval Conference· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Semantic Facets for Scientific Information Retrieval
Iana Atanassova, Marc Bertin
2014· book-chapter· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Textual Entailment - Fitchburg State College.
Orlando Montalvo-Huhn, Stephen Taylor
2008· article· en· Theory and applications of categories· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About