Bibliographic record
Abstract
This paper aims to propose a stochastic response surface method considering correlated input random variables. The orthogonal transform is adopted to treat the correlated random variables in stochastic response surface method. Explicit polynomials are derived for the forth-order and fifth-order Hermite polynomial chaos expansions of random variables. A C#-language based computer program WHUSRSM (Wuhan University Stochastic Response Surface Method) is developed. Four examples are selected to illustrate the application of the proposed stochastic response surface method. The results indicate that the proposed stochastic response surface method can estimate the structural reliability involving correlated random variables efficiently. A third-order stochastic response surface method is reasonably accurate to calculate failure probabilities between 10-3 and 10-4. However, a fourth-order or fifth-order Hermite polynomial chaos expansions should be used for cases with high dependency between input random variables. The number of collocation points equaling twice the number of unknown coefficients does not ensure the accuracy. In general, it is recommended that the number of collocation points should be at least two times of the number of unknown coefficients of the Hermite polynomial chaos expansion.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".