Determining the Number of Excavations Required to Confirm the Presence or Absence of SCC on a Pipeline Following an SCCDA Process
Bibliographic record
Abstract
The pipeline industry has been managing the threat of Stress Corrosion Cracking (SCC) for several years using the methods developed by NACE SCCDA [1] (Stress Corrosion Cracking Direct Assessment) and ASME B31.8S [2] standards. SCCDA is a widely accepted tool for assessing the threat of Stress Corrosion Cracking in pipelines. The process utilizes data from direct examinations at excavations to validate the process as well as to address existing SCC anomalies, if found. However, neither the recommended practices nor the literature provide a usable and practical method for determining the number of excavations necessary for the excavation program based on observed results. To address this question, the Pipeline Research Council International (PRCI) sponsored a project to develop a statistically defendable procedure to determine the number of excavations which would be required to validate the SCCDA process and confirm either the presence or absence of 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".