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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 4 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.

afffundunlabeled
Adaptively Tuned Iterative Low Dose CT Image Denoising
SayedMasoud Hashemi, Narinder Paul, Soosan Beheshti, R.S.C. Cobbold
2015· article· en· Computational and Mathematical Methods in Medicine· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
afffundunlabeled
MRI denoising via phase error estimation
M. Dylan Tisdall, M. Stella Atkins
2005· article· en· Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE· Computer Science
machine prediction:candidate · noneconsensus · none
39
citations
affunlabeled
Denoising by spatial correlation thresholding
Lei Zhang, Paul Bao
2003· article· en· IEEE Transactions on Circuits and Systems for Video Technology· Computer Science
machine prediction:candidate · noneconsensus · none
37
citations
afffundunlabeled
Curvelet‐based ground roll removal
Carson Yarham, Urs Boeniger, Felix J. Herrmann
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
36
citations
affno abstractunlabeled
Finite Discrete Gabor Analysis
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Wavelet Denoising of Coarsely Quantized Signals
S. Neville, N.J. Dimopoulos
2006· article· en· IEEE Transactions on Instrumentation and Measurement· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affno abstractunlabeled
A Self-governing Hybrid Model for Noise Removal
Mohammad Reza Hajiaboli
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations

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