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
Advanced Clustering Algorithms Research
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.

421 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.
421 works in the cohort · of 4,299,418page 3 of 9

Labels cover 0 of 421 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 421 of 421 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
Clustering in the Presence of Background Noise
Shai Ben-David, Nika Haghtalab
2014· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
afffundno abstractunlabeled
Cluster-Based Cumulative Ensembles
Hanan Ayad, Mohamed S. Kamel
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Data bubbles
Markus Breunig, Hans‐Peter Kriegel, Peer Kröger, Jörg Sander
2001· article· en· ACM SIGMOD Record· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Unsupervised Learning: Clustering
Krzysztof J. Cios, Roman W. Świniarski, Witold Pedrycz, Lukasz Kurgan
2007· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
A novel validity measure for clusters of arbitrary shapes and densities
Noha A. Yousri, Mohamed S. Kamel, Mohamed A. Ismail
2008· article· en· Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
On k-means iterations and Gaussian clusters
Renato Cordeiro de Amorim, Vladimir Makarenkov
2023· article· en· Neurocomputing· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
ROSC
Xiang Li, Ben Kao, Siqiang Luo, Martin Ester
2018· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
12
citations
affunlabeled
Efficient Cluster Labeling for Support Vector Clustering
Vincent D'Orangeville, M. Andre Mayers, Mattia Monga, M. Shengrui Wang
2013· article· en· IEEE Transactions on Knowledge and Data Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Efficiently Clustering Documents with Committees
Patrick Pantel, Dekang Lin
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
← previousnext →

How this was built: Screen · Findings · About