Estimating the Financial Risk Reductions Associated with Cathodic Protection Programs
Bibliographic record
Abstract
Abstract On an increasingly frequent basis, pipeline operators are using risk-based decision making to prioritize cross-company expenditures. Due to the long-term mitigation benefits of Cathodic Protection (CP), when planning external corrosion mitigation activities, pipeline operators typically prioritize mitigation of deeper anomalies for integrity expenditures due to their higher Probability of Failure (PoF). However, anomalies that are not receiving adequate CP or those experiencing electrical interference may remain unaddressed using this rationale. This paper presents both a qualitative and semi-quantitative approach to support the quantification of the risk reduction benefits gained from external corrosion prevention on pipelines. This can help in the efficient prioritization of both pro-active and re-active integrity repair activities. Supporting examples are also discussed to help explain the intended use of the methodology and the interpretation of the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".