Effect of Field Operational Variables on Internal Pitting Corrosion of Oil and Gas Pipelines
Bibliographic record
Abstract
Experiments were conducted in six operating oil and gas production pipelines over four years to determine internal pitting corrosion rates under realistic operating conditions. —Pitting corrosion rates were similar when the compositions of surface layers were similar.—When a compact layer of single species formed, the surface was protected from pitting corrosion; the iron sulfide (FeS) layer was more protective than the siderite (FeCO3) layer.—When multiple layers of several species formed, the susceptibility of the surface to pitting corrosion increased. Frequent changes in the pipeline operating conditions facilitated the formation of multiple layers.—When no surface layer formed, the susceptibility of the surface to pitting corrosion decreased but was not eliminated. Extraneous materials (e.g., sand) on the surface facilitated pitting corrosion.—In the absence of surface layer and extraneous materials, no pitting corrosion was observed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".