Some bilateral generating functions involving the Erkus-Srivastava polynomials and some general classes of multivariable polynomials
Bibliographic record
Abstract
Recently, Liu et al. [Bilateral generating functions for the Erkucs-Srivastava polynomials and the generalized Lauricella function, Appl.Math.Comput.218 (2012),pp.7685-7693 investigated some various families of bilateral generating functions involving the Erkucs Srivastava polynomials. The aim of this present paper is to obtain some bilateral generating functions involving the Erkucs-Srivastava polynomials and three general classes of multivariable polynomials introduced earlier by Srivastava in A contour integral involving Fox's H-function, Indian J.Math.14 (1972), pp.1-6, A multilinear generating function for the Konhauser sets of biorthogonal polynomials suggested by the Laguerre polynomials, Pacific J.Math.117 (1985), pp.183-191] and by Kaanouglu and Ozarslan in Two-sided generating functions for certain class of r-variable polynomials, Mathematical and Computer Modelling 54 (2011), pp.625-631. Special cases involving the (Srivastava-Daoust) generalized Lauricella functions are also given.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".