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
Fuzzy Logic and Control Systems
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.

637 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.
637 works in the cohort · of 4,299,418page 3 of 13

Labels cover 1 of 637 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 637 of 637 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.

afffundunlabeled
A Novel Fuzzy System With Dynamic Rule Base
W. Chen, Mehrdad Saif
2005· article· en· IEEE Transactions on Fuzzy Systems· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
affno abstractunlabeled
Fuzzy Logic in Intelligent System Design
Patricia Melín, Oscar Castillo, Janusz Kacprzyk, Marek Reformat, William Melek
2017· book· en· Advances in intelligent systems and computing· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
Fuzzy neural network approach to control systems
Madan M. Gupta, George K. Knopf
2002· article· en· [1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
The genetic development of ordinal sums
Angelo Ciaramella, Roberto Tagliaferri, Witold Pedrycz
2004· article· en· Fuzzy Sets and Systems· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Selecting behaviors using fuzzy logic
François Michaud
2002· article· en· Proceedings of 6th International Fuzzy Systems Conference· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affno abstractunlabeled
Cascade Architectures of Fuzzy Neural Networks
Witold Pedrycz, Marek Reformat, Chunsong Han
2004· article· en· Fuzzy Optimization and Decision Making· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Fuzzy Classification Using Pattern Discovery
Andrew Hamilton-Wright, Daniel W. Stashuk, Hamid R. Tizhoosh
2007· article· en· IEEE Transactions on Fuzzy Systems· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
← previousnext →

How this was built: Screen · Findings · About