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
Transportation and Mobility Innovations
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.

1,357 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.
1,357 works in the cohort · of 4,299,418page 18 of 28

Labels cover 1 of 1,357 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 1,357 of 1,357 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
Information to Go
Keith Penner
2001· article· en· Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
aboutno affunlabeled
Issues in Urban Trip Generation
Kristina Currans
2000· report· en· Engineering
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Study on flying car transportation system
Shengxi Feng, Zeyu Huang, Junhan Wang, Jiayang Xu, Qian Zhang
2023· article· en· Theoretical and Natural Science· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Secondary Drivers
2025· book-chapter· en· IGI Global eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Volunteered Geographic Service
Monica Gentili, Nigel Waters, Muhammad Iqbal Tubbsum, Dennis E. Nicholas
2019· book-chapter· en· Advances in public policy and administration (APPA) book series· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Mobile Virtual Communities of Commuters
Jalal Kawash, Christo El Morr, Hamza Taha, Wissam Charaf
2011· book-chapter· en· IGI Global eBooks· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Who Wants an Automated Vehicle?
David A. Thurlow, Ben D. Sawyer
2019· book-chapter· en· Advances in human and social aspects of technology book series· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Operations Planning in Carsharing Systems
Behnam Izadi
2019· book-chapter· en· Advances in logistics, operations, and management science book series· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
Elite GTA Towing
2025· article· en· Zenodo (CERN European Organization for Nuclear Research)· Engineering
machine prediction:candidate · noneconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About