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
Data Mining Algorithms and Applications
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.

1,100 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.
1,100 works in the cohort · of 4,299,418page 9 of 22

Labels cover 1 of 1,100 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 1,100 of 1,100 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
Linguistic frequent pattern mining using a compressed structure
Jerry Chun‐Wei Lin, Usman Ahmed, Gautam Srivastava, Jimmy Ming‐Tai Wu, Tzung‐Pei Hong, Youcef Djenouri
2021· article· en· Applied Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Preference-Based Frequent Pattern Mining
Moonjung Cho, Jian Pei, Haixun Wang, Wei Wang
2005· article· en· International Journal of Data Warehousing and Mining· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
User-Centered Interactive Data Mining
Yan Zhao, Yaohua Chen, Yiyu Yao
2008· article· en· International Journal of Cognitive Informatics and Natural Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Introduction to Text Mining in Big Data Analytics Minitrack
Derrick L. Cogburn
2017· article· en· Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
aboutno affunlabeled
Big Data or Right Data
Ricardo Baeza‐Yates
2013· article· en· EdShare Southampton (University of Southampton)· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Frequent pattern mining from dense graph streams
Juan J. Cameron, Alfredo Cuzzocrea, Fan Jiang, Carson K. Leung
2014· article· en· Mspace (University of Manitoba)· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Diverging patterns
Aijun An, Qian Wan, Jiashu Zhao
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Association Rules Mining with Auto-encoders
Théophile Berteloot, Richard Khoury, Audrey Durand
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
An Associative Classifier for Uncertain Datasets
Metanat Hooshsadat, Osmar R. Zai͏̈ane
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Subgraph Representation Learning for Team Mining
Radin Hamidi Rad, Ebrahim Bagheri, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Searching for a Black Hole in Tree Networks
Jurek Czyzowicz, Dariusz R. Kowalski, Ευριπίδης Μάρκου, Andrzej Pelc
2005· article· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
← previousnext →

How this was built: Screen · Findings · About