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Record W29582437 · doi:10.1111/trf.14582

Micrological analysis, smooth frames and denoising in Fourier space

2004· book· en· W29582437 on OpenAlexaff
Ryuichi Ashino, Steven J. Desjardins, Christopher Heil, Michihiro Nagase, Rémi Vaillancourt

Bibliographic record

VenueNova Science Publishers, Inc. eBooks · 2004
Typebook
Languageen
FieldMathematics
TopicMathematical Analysis and Transform Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOrthonormal basisWaveletMathematicsMicrolocal analysisNoise reductionSpace (punctuation)Mathematical analysisFourier transformOperator (biology)Frame (networking)Image denoisingOperator theoryComputer scienceArtificial intelligenceFourier integral operatorPhysics

Abstract

fetched live from OpenAlex

Microlocal filtering is performed with adapted orthonormal multiwavelets and smooth frame multiwavelets in Rn. The values of the wavelet coefficients of a function give a rough estimate of its microlocal content, as shown by an example. Multidirectional denoising of images is presented as the action of a pseudodifferential operator which is the product of directional diffusion equations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.346
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2004
Admission routes1
Has abstractyes

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