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
Topic Modeling
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.

2,769 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.
2,769 works in the cohort · of 4,299,418page 6 of 56

Labels cover 6 of 2,769 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 2,769 of 2,769 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
Text Similarity Using Google Tri-grams
Aminul Islam, Evangelos Milios, Vlado Kešelj
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
afffundunlabeled
Contextualized Non-Local Neural Networks for Sequence Learning
Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung
2019· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
MasakhaNER: Named entity recognition for African languages
David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos +54 more
2021· article· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
affunlabeled
Semantic similarity of short texts
Aminul Islam, Diana Inkpen
2009· article· en· Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affno abstractunlabeled
Facet-based opinion retrieval from blogs
Olga Vechtomova
2009· article· en· Information Processing & Management· Computer Science
machine prediction:candidate · noneconsensus · none
47
citations
affunlabeled
How to Get the Most out of Your Curation Effort
Andrey Rzhetsky, Hagit Shatkay, W. John Wilbur
2009· article· en· PLoS Computational Biology· Computer Science
machine prediction:candidate · noneconsensus · none
46
citations

How this was built: Screen · Findings · About