Guidance for Selecting SCC Direct Assessment Locations and Estimation of Re-Inspection Intervals
Bibliographic record
Abstract
A significant amount of research and development has been carried out on the mechanism of the stress corrosion cracking of underground pipelines. This paper describes the results of a study, co-funded by PRCI, the US DOT, and pipelines companies, to bring together the results of these various studies in the form of a set of guidelines that will assist companies in identifying the most likely SCC locations on their systems and in predicting how frequently inspection or other mitigation is required. The guidelines have been developed along mechanistic lines, and are divided into four “steps” representing: susceptibility to SCC, crack initiation, early-stage growth and dormancy, and crack growth to failure. For each step, a series of Research Guidelines has been derived from the results of individual research papers or studies. These Research Guidelines may or may not be easily validated against field data. The SCC Guidelines were then developed based on one or more Research Guidelines. Wherever possible, the SCC Guidelines have been validated against field data, but in some cases currently un-testable SCC Guidelines were defined because they offer a potentially unique opportunity to identify where and when SCC might occur.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".