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Record W4399020226 · doi:10.1007/s00208-024-02887-9

Multipliers on bi-parameter Haar system Hardy spaces

2024· article· lv· W4399020226 on OpenAlexafffund
Richard Lechner, Pavlos Motakis, Paul F. X. Müller, Th. Schlumprecht

Bibliographic record

VenueMathematische Annalen · 2024
Typearticle
Languagelv
FieldMathematics
TopicAdvanced Harmonic Analysis Research
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaAustrian Science FundNational Science Foundation
KeywordsMathematicsHaarHardy spacePure mathematicsAlgebra over a fieldMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Let $$(h_I)$$ ( h I ) denote the standard Haar system on [0, 1], indexed by $$I\in \mathcal {D}$$ I ∈ D , the set of dyadic intervals and $$h_I\otimes h_J$$ h I ⊗ h J denote the tensor product $$(s,t)\mapsto h_I(s) h_J(t)$$ ( s , t ) ↦ h I ( s ) h J ( t ) , $$I,J\in \mathcal {D}$$ I , J ∈ D . We consider a class of two-parameter function spaces which are completions of the linear span $$\mathcal {V}(\delta ^2)$$ V ( δ 2 ) of $$h_I\otimes h_J$$ h I ⊗ h J , $$I,J\in \mathcal {D}$$ I , J ∈ D . This class contains all the spaces of the form X(Y), where X and Y are either the Lebesgue spaces $$L^p[0,1]$$ L p [ 0 , 1 ] or the Hardy spaces $$H^p[0,1]$$ H p [ 0 , 1 ] , $$1\le p < \infty $$ 1 ≤ p < ∞ . We say that $$D:X(Y)\rightarrow X(Y)$$ D : X ( Y ) → X ( Y ) is a Haar multiplier if $$D(h_I\otimes h_J) = d_{I,J} h_I\otimes h_J$$ D ( h I ⊗ h J ) = d I , J h I ⊗ h J , where $$d_{I,J}\in \mathbb {R}$$ d I , J ∈ R

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.002

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 head0.080
GPT teacher head0.365
Teacher spread0.285 · 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
Published2024
Admission routes2
Has abstractyes

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