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
Institutional Repositories DataBase (IRDB)
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.

1,957 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.
1,957 works in the cohort · of 4,299,418page 32 of 40

Labels cover 1 of 1,957 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 1,957 of 1,957 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.

aboutno affunlabeled
HIROSHIMA RESEARCH NEWS(19)
Takeshi Ishida, Seiichiro TAKEMINE, Sung Chull KIM, Kazumi Mizumoto, Narayanan GANESAN, JIN XIDE +1 more
2004· other· en· Institutional Repositories DataBase (IRDB)
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
バークの『カトリック法論』
正己 真嶋, Masami Majima
2008· article· ja· Institutional Repositories DataBase (IRDB)· Arts and Humanities
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
Trends in Canada's Science and Technology Policy
敦 小笠原
2002· article· en· Institutional Repositories DataBase (IRDB)· Decision Sciences
machine prediction:candidate · metaresearch+stsconsensus · none
0
citations
affunlabeled
The Good, Bad, and Ugly of Academic Writing
Daniel H. Brooks
2023· article· Institutional Repositories DataBase (IRDB)· Arts and Humanities
machine prediction:candidate · metaresearchconsensus · none
0
citations
aboutno affunlabeled
清涼飲料水中のカフェイン含有量について
禎浩 川添, 百花 松下, 芽來 松尾, 淑美 山田, 瑠菜 大淵, 裕子 坂口 +1 more
2022· article· ja· Institutional Repositories DataBase (IRDB)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affno abstractunlabeled
A Note on the Regional Geography of Canada
太郎 大石, Taro Oishi
2012· article· ja· Institutional Repositories DataBase (IRDB)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
2009年度ケベックスタージュ報告
和子 太治
2010· article· ja· Institutional Repositories DataBase (IRDB)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
創造都市の大阪モデルを求めて
Masayuki Sasaki
2006· article· ja· Institutional Repositories DataBase (IRDB)· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
2015年カナダ連邦選挙の分析
Nobuaki Suyama
2016· article· ja· Institutional Repositories DataBase (IRDB)· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About