Development of a Predictive Model for the Initiation and Early-Stage Growth of Near-Neutral pH SCC of Pipeline Steels
Bibliographic record
Abstract
Abstract The format of a model designed to predict the probability of near-neutral pH SCC initiation on operating pipelines is described. The model would be used in conjunction with existing site-selection models to improve the prioritization of SCC excavations and to increase the probability of locating SCC in the field. Information being developed as part of the program could also be used to design and select improved alloys and coating systems for new construction. The model is based on the results of a laboratory program aimed at identifying factors that lead to SCC initiation and early-stage crack growth, resulting in "viable" cracks. To date, five factors have been identified that lead to the earliest indications of crack initiation: inclusions, aligned defects, pre-existing defects on the pipe surface, persistent slip bands produced by mechanical pre-treatment of the steel, and coating disbondment. These factors produce defects on the surface several grains (~10's of μm) in length. The growth of these crack initiation events into definite cracks (defined as ≥100 μm in length) is being studied. Although not specifically studied in the program, residual stress is also known to affect crack initiation and early-stage growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".