Prediction of Corrosion Defect Growth on Operating Pipelines
Why this work is in the frame
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Bibliographic record
Abstract
Integrity management is based on the ability of the pipeline operator to predict the growth of defects detected in inspection programs on an operating pipeline system. Accurate predictions allow targeted interventions to be scheduled in a cost effective and timely fashion for those defects that pose a high potential risk. In this paper two distinct theories are described for predicting the development of corrosion pits on an operating pipeline. The first theory corresponds to the traditional approach in which the past growth behaviour of each defect is used to predict the rate of its future development. In this theory each defect is assumed to have its own unique corrosion environment in which only a very limited range of corrosion rates will be seen. In the second approach, this assumption is not made. Instead any corrosion defect is allowed to grow at any likely rate over any time interva. In this approach an arbitrary selection of corrosion rates derived from the overall profile of past rates seen for all defects is applied to each defect over time. Predicted distributions derived by computer simulation of the initiation and growth of corrosion defects according to each theory have been compared to an actual defect depth distribution derived by in line inspection (ILI) of an operating pipeline. The success of the two models is compared and implications for pipeline integrity management are discussed.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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