The Role of Coatings in the Generation of High- and Near-Neutral pH Environments That Promote Environmentally Assisted Cracking
Bibliographic record
Abstract
Abstract Accurately predicting where high- or near-neutral pH stress corrosion cracking (SCC) of buried pipelines is possible requires a prediction of the environmental conditions at the pipe surface under the disbonded coating. This is a challenging task since traditional above-ground measurements give little information about the pipe-surface environment. If such predictions could be made, however, the success of selecting locations for direct examination as part of the direct assessment methodology or for other maintenance activities would be greatly enhanced. The nature of the coating degradation has a significant impact on the development of environmental conditions for SCC. A series of soil box tests has been performed to determine the evolution of the trapped water environment under disbonded shielding polyethylene tape coating. The effects of soil type, moisture content and drainage, and CP level have been studied. In a second series of tests, the relative effects of current demand and coating permeability on the generation of high-pH SCC conditions under disbonded permeable coating have been studied. The results of the tests on shielding coating have been analyzed using two computer models; a Transient ElectroChemical TRANsport model (TECTRAN) for shielding coating and the Permeable Coating Model (PCM) for permeable coatings. The aim is to use such codes to predict the pipe surface environment based only on above-ground measurements and other readily available information.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".