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
Business Process Modeling and Analysis
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.

844 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.
844 works in the cohort · of 4,299,418page 4 of 17

Labels cover 0 of 844 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 844 of 844 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.

affunlabeled
Mashup environments in software engineering
Lars Grammel, Christoph Treude, Margaret‐Anne Storey
2010· article· en· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A business process explorer
Jin Guo, King Chun Foo, Liliane Barbour, Ying Zou
2008· article· en· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Business process mining
Asef Pourmasoumi, Ebrahim Bagheri
2017· article· en· Encyclopedia with Semantic Computing and Robotic Intelligence· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Product Recalls: A Review of Literature
Muralidharan Etayankara, Hari Bapuji
2009· review· en· ASAC· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
PS: A Policy Simulator
Issam Aib, Raouf Boutaba
2007· article· en· IEEE Communications Magazine· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Characterizing Timeout Builds in Continuous Integration
Nimmi Rashinika Weeraddana, Mahmoud Alfadel, Shane McIntosh
2024· article· en· IEEE Transactions on Software Engineering· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
9
citations
affno abstractunlabeled
Rule-based validation of SLA choreographies
Irfan Ul Haq, Adrian Paschke, Erich Schikuta, Harold Boley
2010· article· en· The Journal of Supercomputing· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Learning Scheduling Models from Event Data
Arik Senderovich, Kyle E. C. Booth, J. Christopher Beck
2019· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
8
citations
← previousnext →

How this was built: Screen · Findings · About