Some zero-balanced terminating hypergeometric series and their applications
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".