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Record W4398237617 · doi:10.2298/fil2322367s

Some zero-balanced terminating hypergeometric series and their applications

2023· article· en· W4398237617 on OpenAlexaff
H. M. Srivastava, Shakir Hussain Malik, M. I. Qureshi, Bilal Ahmad Bhat

Bibliographic record

VenueFilomat · 2023
Typearticle
Languageen
FieldMathematics
TopicMathematical functions and polynomials
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMathematicsNormalization propertySeries (stratigraphy)Lauricella hypergeometric seriesZero (linguistics)Basic hypergeometric seriesHypergeometric functionPure mathematicsGeneralized hypergeometric functionHypergeometric function of a matrix argumentLinguisticsComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Various families of such Special Functions as the hypergeometric functions of one, two and more variables, and their associated summation, transformation and reduction formulas, are potentially useful not only as solutions of ordinary and partial differential equations, but also in the widespread problems in the mathematical, physical, engineering and statistical sciences. The main object of this paper is first to establish four general double-series identities, which involve some suitably-bounded sequences of complex numbers, by using zero-balanced terminating hypergeometric summation theorems for the generalized hypergeometric series r+1Fr(1) (r = 1, 2, 3) in conjunction with the series rearrangement technique. The sum (or difference) of two general double hypergeometric functions of the Kamp? de F?riet type are then obtained in terms of a generalized hypergeometric function under appropriate convergence conditions. A closed form of the following Clausen hypergeometric function: 3F2 (?27z/4(1?z)3) and a reduction formula for the Srivastava-Daoust double hypergeometric function with the arguments (z,?z/4 ) are also derived. Many of the reduction formulas, which are established in this paper, are verified by using the software program, Mathematica. Some potential directions for further researches along the lines of this paper are also indicated.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.387

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 head0.043
GPT teacher head0.295
Teacher spread0.253 · 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 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
Published2023
Admission routes1
Has abstractyes

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