Effects of Non-Normal Input Distributions and Sampling Region on Monte Carlo Results
Bibliographic record
Abstract
In order to address the risks associated with the operation of ageing pressure boundary components, many assessments incorporate probabilistic analysis tools for alleviating excessive conservatism of deterministic methodologies. In general, deterministic techniques utilize conservative bounding values for all critical parameters. Recently, various Probabilistic Fracture Mechanics (PFM) codes have been employed to identify governing parameters which could affect licensing basis margins of pressure retaining components. Moreover, these codes are used to calculate a probability of failure in order to estimate potential risks under operating and design loading conditions for the pressure retaining components experiencing plausible and active degradation mechanisms. Probabilistic approaches typically invoke the Monte-Carlo (MC) method where a set of critical input variables are randomly distributed and inserted in deterministic computer models. Estimates of results from probabilistic assessments are then compared against various assessment criteria. During the PVP-2016 conference, we investigated the assumption of normality of the Monte Carlo results utilizing a non-linear system function. In this paper, we extend the study by employing non-normal input distributions and investigating the effects of sampling region on the system function.
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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 | 0.025 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".