Development of Guidelines for the Identification of SCC Sites and the Prediction of Re-Inspection Intervals for SCC DA
Bibliographic record
Abstract
Abstract A significant amount of research and development (R&D) has been carried out on the mechanism of the stress corrosion cracking (SCC) of underground pipelines since the phenomenon was first recognized in the 1960’s. This R&D has taken the form of both laboratory-based experimental investigations and the direct measurements and observations of SCC on operating pipeline systems. Correlation of these data sets may be useful for predicting the occurrence and severity of SCC in the field. An effort is underway, co-funded by Pipeline Research Council International (PRCI), the US DOT, and pipeline companies, to consolidate the results of these various studies in the form of a set of guidelines that will assist companies in locating SCC on their systems, establishing the frequency of inspection intervals, and predicting the need for and timing for mitigation. The guidelines are being developed along mechanistic lines, and are broken down into four “stages” or “modules” representing: susceptibility to SCC, crack initiation, early-stage growth and dormancy, and crack growth to failure. A key component of the work is the validation of the guidelines developed from the R&D literature against field data. This interim report on the work describes the format of the guidelines and the progress made to date in developing guidelines and validating them against field data. Ultimately, it is hoped that these guidelines will be used in future revisions of the NACE SCC DA Recommended Practice RP-0204.
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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 | 0.046 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".