A simulation of probability of rejection as an aid to understanding thesignificance of sizing accuracy
Bibliographic record
Abstract
In Quantitative Nondestructive Evaluation (QNDE), assessing the reliability of the NDT method is crucial. Technology advances and the development of new life estimation models based on the damage tolerance concept have led to the maturing of QNDE techniques. Nowadays, the concept is widely used for such models in fitness-for-service (FFS) assessments. As an input to these models, flaws size plays a very important role. In these concepts, Probability of Rejection (PoR) was introduced around 2007 and combined the concepts of Probability of Detection (PoD) with the need to accurately size flaws when using fracture mechanics-based acceptance criteria. Improvements in sizing techniques have been made and fracture-mechanics acceptance criteria are becoming more commonly accepted instead of the traditional workmanship criteria. However, experimental PoD campaigns are excessively time and money-consuming, rapidly making them almost prohibitive. On the other hand, recent advances in technology to accurately simulate nondestructive testing (NDT) processes made available new tools for reliability study. This paper uses CIVA’s ultrasonic inspection simulation to demonstrate how small changes on the flaw sizing characterization would affect probability of rejection.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".