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
Sparse and Compressive Sensing Techniques
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.

892 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.
892 works in the cohort · of 4,299,418page 5 of 18

Labels cover 1 of 892 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 892 of 892 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
Cost-sensitive Multiclass Classification Risk Bounds
Bernardo Avila Pires, Mohammad Ghavamzadeh, Csaba Szepesvári
2013· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Engineering
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Fast Group Sparse Classification
Angshul Majumdar, Rabab Ward
2009· article· en· Engineering
machine prediction:candidate · noneconsensus · none
21
citations
afffundunlabeled
Weighted-ℓ 1 minimization with multiple weighting sets
Hassan Mansour, Özgür Yılmaz
2011· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Engineering
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
ACDC: A Structured Efficient Linear Layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard, Nando de Freitas
2015· preprint· en· arXiv (Cornell University)· Engineering
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Coded Acquisition of High Frame Rate Video
Reza Pournaghi, Xiaolin Wu
2014· article· en· IEEE Transactions on Image Processing· Engineering
machine prediction:candidate · noneconsensus · none
18
citations
fundno affunlabeled
Inverse Optimization with Noisy Data
Anil Aswani, Zuo‐Jun Max Shen, Auyon Siddiq
2018· preprint· en· Operations Research· Engineering
machine prediction:candidate · noneconsensus · none
18
citations
← previousnext →

How this was built: Screen · Findings · About