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Majorization in Analytic Functions Among Distinct Classes Defined by Modified Tremblay Fractional Operator

2024· article· en· W4400821874 on OpenAlexvenueno aff
Indushree Mohan, Madhu Venkataraman

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2024
Typearticle
Languageen
FieldMathematics
TopicIterative Methods for Nonlinear Equations
Canadian institutionsnot available
Fundersnot available
KeywordsMajorizationMathematicsFractional calculusOperator (biology)Pure mathematicsApplied mathematicsMathematical economicsCalculus (dental)EconometricsDiscrete mathematicsMedicine

Abstract

fetched live from OpenAlex

This paper presents and investigates three distinct kinds of analytic functions described by the Modified Tremblay Fractional Operator: IΥ[A,B], QΥ[A,B], and PΥ[A,B]. We give a detailed knowledge of these unique categories features by exploring majorization difficulties within them. By means of a careful analysis of majorization phenomena, we present a range of novel findings that demonstrate the significance of parameter specialisation in these classes. This work greatly expands our understanding of analytic functions and improves the field of mathematical analysis as a whole. To sum up, this study offers a comprehensive investigation of new analytic function classes, clarifies certain aspects of majorization, and makes significant contributions that broaden our understanding of complex analysis and geometric function theory.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0010.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.373
Teacher spread0.341 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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