Mechano‐electrochemical Interaction for Level III Assessment of Corrosion Anomalies on Pipelines – Multiple Corrosion Defects
Bibliographic record
Abstract
Multiple corrosion defects are present on pipelines and frequently interact with each other. The M–E interactions occur not only at specific defects but also at the adjacency between the corrosion defects, further affecting the pipeline fitness-for-service (FFS) and failure pressure. Therefore, Level III defect assessment by integrating the M–E interaction should include mutual interaction between adjacent corrosion defects in consideration. This chapter imparts Level III assessment on multiple corrosion defects with the consideration of M–E interaction at the defects and their interaction. The defects are oriented on pipelines longitudinally, circumferentially, or overlapped, for which new interaction rules are developed for more accurate assessment of the maximum stress concentration and the associated anodic current density (i.e., corrosion rate). Moreover, the corrosion defects which are oriented irregularly on pipelines are also modeled to determine the M–E interaction and its effect on pipeline failure, while the mutual interaction between the defects is considered in modeling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".