Benchmarking of Three Girth Weld Flaw Assessment Models: A Validation Methodology
Bibliographic record
Abstract
Abstract The application of quantitative risk assessment involves the utilization of pre-existing fitness for service calculators. This departure from the original intent of these calculators necessitates a comprehensive evaluation of their efficacy within an extended domain. This article elucidates the methodology employed to gauge the performance of three girth weld flaw assessment models: namely, the Kastner model, API 1104 Appendix A Option 2, and API 579. The proposed framework delineates a comprehensive analysis, originally devised for financial models, surpassing the conventional validation process, which typically entails a comparison of predicted values against selectively chosen experimental data, commonly referred to as the “least square error Olympics”. A sensitivity analysis of the models was employed to evaluate the impact of data sparseness and ascertain the optimal data collection strategy. Furthermore, we advocate for the measurement of the decrease in entropy as a more effective means of demonstrating the predictive information encapsulated within distinct models. The benchmarking exercise revealed that the venerable Kastner model exhibits a confined range of applicability but maintains a high level of accuracy. Additionally, it was observed that there are instances where a synergistic combination of models can be employed to enhance predictive capabilities.
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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 | 0.011 | 0.018 |
| 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) | 0.001 | 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".