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
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 4 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.

affno abstractunlabeled
The convergence of the Weiszfeld algorithm
Halit Üster, Robert F. Love
2000· article· en· Computers & Mathematics with Applications· Engineering
machine prediction:candidate · noneconsensus · none
29
citations
afffundno abstractunlabeled
Stable recovery of analysis based approaches
Yi Shen, Bin Han, Elena Braverman
2014· article· en· Applied and Computational Harmonic Analysis· Engineering
machine prediction:candidate · noneconsensus · none
29
citations
afffundunlabeled
Matrix Linear Discriminant Analysis
Wei Hu, Weining Shen, Hua Zhou, Dehan Kong
2019· article· it· Technometrics· Engineering
machine prediction:candidate · noneconsensus · none
28
citations
affunlabeled
Deep learning methods for inverse problems
Shima Kamyab, Zohreh Azimifar, Rasool Sabzi, Paul Fieguth
2022· article· en· PeerJ Computer Science· Engineering
machine prediction:candidate · noneconsensus · none
28
citations
afffundno abstractunlabeled
Stability of the elastic net estimator
Yi Shen, Bin Han, Elena Braverman
2015· article· en· Journal of Complexity· Engineering
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Compressed Network Monitoring
Mark Coates, Yvan Pointurier, Michael Rabbat
2007· article· en· 2007 IEEE/SP 14th Workshop on Statistical Signal Processing· Engineering
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
FeaFiner
Jiayu Zhou, Zhaosong Lu, Jimeng Sun, Lei Yuan, Fei Wang, Jieping Ye
2013· article· en· Engineering
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Recovery probability analysis for sparse signals via OMP
Mingbo Niu, Soheil Salari, Il‐Min Kim, François Chan, Sreeraman Rajan
2015· article· en· IEEE Transactions on Aerospace and Electronic Systems· Engineering
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Compressed Least-Squares Regression on Sparse Spaces
Mahdi Milani Fard, Yuri Grinberg, Joëlle Pineau, Doina Precup
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Engineering
machine prediction:candidate · noneconsensus · none
23
citations

How this was built: Screen · Findings · About