Pitting and Corrosion Rates of Coated, Uncoated, and Insulated A333 Steel Pipelines in Marine Harsh Environment
Bibliographic record
Abstract
Abstract Pipelines are one of the most economical and safe means of transporting useful materials, and their design life depends on protection mechanisms present. Marine environments increase corrosion rates due to moisture, and elements like chloride which increase localized pitting rates. Thirty-six A333 low temperature carbon steel pipelines were placed at Argentia, NL, an extremely corrosive environment (C5) near high tide mark. The experiment consisted of coated, uncoated, and insulated pipes. Exposed for a period of two years, corrosion rate, optical inspections, and pit depth were recorded at intervals. The highest average pit and maximum pit depth occurred in uncoated insulated pipes and coated uninsulated pipes. The highest average mass loss occurred in uncoated (insulated and uninsulated) pipes. The least mass loss and pit depths generally occurred in coated pipes (both insulated and uninsulated). Corrosion near the ends of the pipes were more significant than other locations. Final averaged corrosion rates for insulated coated and uncoated pipes, were 0.017 and 0.021mm/yr respectively. Corrosion rates for uninsulated coated and uncoated pipes, were 0.014mm/yr and 0.023mm/yr respectively. Maximum and mean pit depths for insulated coated and uncoated pipes were, 180/156 and 256/205 microns, respectively, while for uninsulated coated and uncoated were 210/177 and 182/148 microns, respectively. Some coated and uncoated insulated pipes had negligible pitting and corrosion. Results provide an increased understanding of corrosion rates, corrosion under insulation corrosion, and under coating, and pitting data for pipelines in service in marine harsh environments.
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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 | 0.000 |
| 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) | 0.001 | 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".