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 8 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Anomaly Detection for Mobile Device Comfort
Mehmet Vefa Bicakci, Babak Esfandiari, Stephen Marsh
2014· book-chapter· en· IFIP advances in information and communication technology· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
8
citations
affno abstractunlabeled
Efficiently Mining Regional Outliers in Spatial Data
Richard Frank, Wen Jin, Martin Ester
2007· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · none
8
citations
affunlabeled
Log Message Anomaly Detection with Oversampling
Amir Farzad, T. Aaron Gulliver
2020· article· en· International Journal of Artificial Intelligence & Applications· Computer Science
distilled prediction:candidate · noneconsensus · none
8
citations
afffundunlabeled
Detecting Semantic Anomalies
Faruk Ahmed, Aaron Courville
2020· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
affno abstractunlabeled
Simplified PCNet with robustness
Bingheng Li, Xuanting Xie, Haoxiang Lei, Ruiyi Fang, Zhao Kang
2024· article· en· Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations

How this was built: Screen · Findings · About