Correlating Corrosion Field Data With Experimental Findings for the Development of Pipeline Mitigation Strategies
Bibliographic record
Abstract
Coating disbondment on pipelines is a common phenomenon that leads to exposure of the pipeline metal to ground water solutions, promoting a corrosive environment which is associated with stress corrosion cracking (SCC). This investigation aims to understand the corrosion behavior and rate of pipeline steel under coating disbondments of varying sizes based on field data and experimental studies. In the analysis of the field data, dig reports provided by a Canadian gas transportation company were analyzed for cases of anaerobic corrosion under tape coatings. The analyzed field data provided a correlation between the tape coating disbondment size and corrosion rate found under the coating. The experimental studies aimed to understand the field findings. The analyses were performed on X-65 pipeline steel coupons placed in a vertical coupon holder with a PMMA shielding. To imitate the variation in the disbondment size, the gap size between the metal coupons and the shielding was varied (2 mm, 5 mm, and 10 mm, and infinite). The general corrosion rates were obtained through weight loss calculations. The experimental results were compared and correlated with dig-report data from the field for a development of cathodic protection and pipeline mitigation strategies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".