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Record W2112865059 · doi:10.1109/sampta.2015.7148880

The structure of test functions that determine weighted composition operators

2015· article· en· W2112865059 on OpenAlexaff
Peter C. Gibson, Mohammad S. Tavalla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicMathematical Analysis and Transform Methods
Canadian institutionsYork University
Fundersnot available
KeywordsContext (archaeology)Operator (biology)Computer scienceAlgorithmConstruct (python library)SIGNAL (programming language)Composition (language)Identification (biology)Test (biology)MathematicsPattern recognition (psychology)Artificial intelligenceGeology

Abstract

fetched live from OpenAlex

Operators that preserve minimum phase signals, or delayed minimum phase signals, have been shown to be important in practical signal processing contexts, and specifically in geophysical imaging, where one seeks to identify such operators using test signals. Which sets of test signals suffice to recover an unknown operator of the given type? In the present paper we answer this question by relating it to the identification of weighted composition operators acting on analytic functions on the disk. We provide an explicit parameterization of all minimal sets of test functions that identify weighted composition operators on the disk, and then apply the parameterization to construct realistic test signals for use in the geophysical context.

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)0.002
metaresearch head score (Gemma)0.010
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.070
GPT teacher head0.330
Teacher spread0.260 · 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
GenreEmpirical

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

Citations1
Published2015
Admission routes1
Has abstractyes

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