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Record W4388204568 · doi:10.18280/mmep.100508

Higher-Order Derivatives of Differential Subordination of Multivalent Functions

2023· article· en· W4388204568 on OpenAlexvenueno aff
Mustafa I. Hameed, Shaheed Jameel Al-Dulaimi, Hussaini Joshua

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldMathematics
TopicAnalytic and geometric function theory
Canadian institutionsnot available
Fundersnot available
KeywordsSubordination (linguistics)Differential (mechanical device)Order (exchange)MathematicsApplied mathematicsPhilosophyEconomicsPhysicsLinguistics

Abstract

fetched live from OpenAlex

The research into theory for analytic univalent as well as multivalent functions is an ancient subject for mathematics, especially in complex analysis, which has attracted a great number for scholars due to utter elegance of the its geometrical characteristics as well as numerous research opportunities.The study of univalent functions is one of most important areas of complex analysis for only one and many variables.Researchers have been interested in the traditional study of this subject since at least 1907.During this time until now many researchers in the field of complex analysis, including as Euler, Gauss, Riemann, Cauchy, and many others, have developed.Geometric function theory is a combination or interplay of geometry and analysis.The main goal of this article is to investigate the principle for dependence as well as add an additional subset for polyvalent functions with a different operator that is related to derivatives of higher order.As a result, the findings were important in terms of various geometric properties, including coefficient estimation, distortion as well as growth borders, radii for starlikeness, convexity, as well as close-to-convexity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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

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 head0.054
GPT teacher head0.258
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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