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
Image and Signal Denoising Methods
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.

1,335 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.
1,335 works in the cohort · of 4,299,418page 6 of 27

Labels cover 0 of 1,335 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 1,335 of 1,335 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
Adaptive video filtering framework
Rastislav Lukàč, Viktor Fischer, G. Motyl, Miloš Drutarovský
2004· article· en· International Journal of Imaging Systems and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
Riesz multiwavelet bases
Bin Han, Soon-Geol Kwon, Sang Soo Park
2005· article· en· Applied and Computational Harmonic Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
An Object-Based Method for Rician Noise Estimation in MR Images
Pierrick Coupé, José V. Manjón, Elias Gedamu, Douglas L. Arnold, Montserrat Robles, D. Louis Collins
2009· article· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Modeling sRGB Camera Noise with Normalizing Flows
Shayan Kousha, Ali Maleky, Michael S. Brown, Marcus A. Brubaker
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
A Characterization of Stockwell Spectra
M. W. Wong, Hongmei Zhu
2006· book-chapter· en· Birkhäuser Basel eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Wavelet filters and infinite-dimensional unitary groups
Donggao Deng, Daren Huang, Rong-Qing Jia, Lin Wei, Jianzhong Wang
2002· book-chapter· en· AMS/IP studies in advanced mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Convergence rates of cascade algorithms
Rong-Qing Jia
2003· article· lv· Proceedings of the American Mathematical Society· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
← previousnext →

How this was built: Screen · Findings · About