A Bayesian inference based simulation approach for estimating fraction nonconforming of pipe spool welding processes
Why this work is in the frame
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Bibliographic record
Abstract
Pipe spool fabrication is the most vital process to the successful delivery of industrial construction project. Due to the various combinations of pipe attributes in terms of Nominal Pipe Size (NPS), Pipe Schedule, and material, it is hard for practitioners to estimate the pipe welding quality performance based on the available historical data. This paper aims to develop a Bayesian Inference based simulation approach to assist making good estimates of welds fraction nonconforming for proposing a new project to clients. In this proposed approach, the pipe welding inspection process is first modeled as a Bernoulli process. Utilizing the tracked historical inspection data, Jeffreys Intervals are estimated for determining the distributions of welds fraction nonconforming. These distributions can serve as the inputs for Monte Carlo Simulation to incorporate uncertainties for fabricators' decision-making process. The simulation results demonstrate good reliability and accuracy compared to the actual project welds repair rates.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it