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
Requirements Engineering
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.

70 results · 1 filter active ·
Results by year
20012025
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.
70 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 70 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 70 of 70 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
Specifying and analyzing early requirements in Tropos
Ariel Fuxman, Lin Liu, John Mylopoulos, Marco Pistore, Marco Roveri, Paolo Traverso
2004· article· en· Requirements Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
243
citations
affno abstractunlabeled
Patterns of continuous requirements clarification
Eric Knauss, Daniela Damian, Jane Cleland‐Huang, Remko Helms
2014· article· en· Requirements Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
User?s manual as a requirements specification: case studies
Daniel M. Berry, Khuzaima Daudjee, Jing Dong, I. Fainchtein, Milo Nelson, Torsten Nelson +1 more
2004· article· en· Requirements Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
afffundno abstractunlabeled
Unified use case statecharts: case studies
Davor Svetinović, Daniel M. Berry, Nancy A. Day, Michael W. Godfrey
2007· article· en· Requirements Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
next →

How this was built: Screen · Findings · About