Development of SCC Susceptibility Model Using Decision Tree Approach
Bibliographic record
Abstract
Abstract Stress corrosion cracking (SCC) on pipelines has been extensively studied over the past three to four decades. Various models have been developed to predict where and how fast SCC occurs on pipelines. However, due to the complexity of SCC, no general models are currently available to accurately predict SCC on pipelines. Models developed based on operating experience for one geographic location has often performed poorly in another region. For example, the SCC soils model developed in the past predicts that low-pH SCC will occur in poorly drained, anaerobic soils; however, in the same general geographic region, low-pH SCC has also been found to occur preferentially in well-drained soils. It is therefore critical to collect all related data and understand the actual SCC mechanism to develop an effective SCC susceptibility model that will be more generally applicable. This paper introduces a data mining methodology, a decision tree approach, for identification of the correlation between the presence of SCC and environmental/ loading conditions and further refinement of SCC susceptibility with mechanistic understanding.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".