MétaCan
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.

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
Anomaly Detection Techniques 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.

1,158 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.
1,158 works in the cohort · of 4,299,418page 20 of 24

Labels cover 1 of 1,158 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 1,158 of 1,158 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
Repairs and Breaks Prediction for Deep Neural Networks
Yuta Ishimoto, Masanari Kondo, Lei Ma, Naoyasu Ubayashi, Yasutaka Kamei
2024· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Streaming Isolation Forest
J. LIU, Guilherme Weigert Cassales, Fei Tony Liu, Bernhard Pfahringer, Albert Bifet
2025· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Global tests for novelty
Ilmari Ahonen, Denis Larocque, Jaakko Nevalainen
2015· article· en· Statistical Methods in Medical Research· Computer Science
machine prediction:candidate · metaresearchconsensus · none
0
citations
aboutno affunlabeled
An Algorithm for the Initial Detection of Malicious Traffic Based on the Autoencoder Reconstruction Error and a Variational Model: the Influence of the Error Distribution Density on the Performance Indicators of the Models
Adeyemi Marc Aurele Emmanuel Djeguede
2025· article· en· Вестник Пермского университета Математика Механика Информатика· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
Crowd understanding and analysis
Qi Wang, Bo Liu, Jianzhe Lin
2021· article· en· IET Image Processing· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Deep learning
Mohammad Ali Ahmadi
2024· book-chapter· en· Elsevier eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Incident Analysis for AI Agents
Carson Ezell, Xavier Roberts-Gaal, Alan Chan
2025· article· en· Proceedings of the AAAI/ACM Conference on AI Ethics and Society· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Evaluation of Internal Leak Detection Techniques
Shawn Learn, Yue Cheng, Ryan Dolan, Seyedehsan Shahidi
2015· article· en· PSIG Annual Meeting· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About