A Statistical Risk Model to Predict the Occurrence of SCC
Bibliographic record
Abstract
Abstract Near-neutral stress corrosion cracking (SCC) is an operational integrity problem experienced by pipeline transportation companies since the 1970’s. Pipeline operators have used a number of different methods to predict and locate SCC. Current in-line inspection technology allows for the detection of SCC in pipelines using ultrasonic measurement. However, these tools have size limitations (not available for small diameter pipelines) and can only accurately detect cracks above a certain threshold dimension. To date, predictive models have focused mainly on establishing quantitative relationships between environmental factors and SCC formation and growth. In general, the models used to predict SCC growth have been more successful than the models used to predict the location of SCC formation. In contrast to previous models that attempted to determine direct relationships between environmental parameters and SCC formation, a model has been developed by statistically analyzing data pertaining to locations along a pipeline where SCC was and was not found during field investigations. The data was analyzed using statistical regression techniques and a multi-variable logistic regression model was created. The model was then applied to a pipeline and verification digs were conducted. The results of the verification digs indicate that the model is able to accurately predict locations with SCC.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".