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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.

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Image and Signal Denoising Methods
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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.

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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 ·
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20002025
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Machine labels · sparse coverage
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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 7 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.

afffundno abstractunlabeled
Fully hyperbolic convolutional neural networks
Keegan Lensink, Bas Peters, Eldad Haber
2022· article· en· Research in the Mathematical Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Edge‐preserving image denoising
Fenghua Guo, Caiming Zhang, Mingli Zhang
2018· article· en· IET Image Processing· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Mean Square Error Estimation in Thresholding
Soosan Beheshti, Masoud Hashemi, Ervin Sejdić, Tom Chau
2010· article· en· IEEE Signal Processing Letters· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Nonlinear filtering for phase image denoising
Juan V. Lorenzo‐Ginori, Konstantinos N. Plataniotis, A.N. Venetsanopoulos
2002· article· en· IEE Proceedings - Vision Image and Signal Processing· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affunlabeled
Application of Ghost-DeblurGAN to Fiducial Marker Detection
Yibo Liu, Amaldev Haridevan, Hunter Schofield, Jinjun Shan
2022· article· en· 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
BayesShrink Ridgelets for Image Denoising
Nezamoddin Nezamoddini-Kachouie, Paul Fieguth, Edward Jernigan
2004· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Can Lower Resolution Be Better?
Xiangjun Zhang, Xiaolin Wu
2008· article· en· DCC· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations

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