Localized Dissolution of Millscale-Covered Pipeline Steel Surfaces
Bibliographic record
Abstract
Electrochemical impedance spectroscopy, corrosion potential measurements, and surface analysis by scanning electron microscopy/energy-dispersive x-ray spectroscopy (SEM/EDX) and Raman spectroscopy were used to investigate the localized dissolution of millscale-covered pipeline steel surfaces. The porous millscale originally present on the pipe surface exerts an influence on the corrosion of the pipeline and may contribute to the eventual onset of stress corrosion cracking (SCC). Three regions in the corrosion potential-time plot were observed after exposure to an aqueous environment, corresponding to the initial attempts at breakdown of the millscale, coupling of the dissolution of the underlying steel to reductive dissolution of the millscale, and active corrosion of the steel at the base of pores in the film supported by water reduction either on the metal or on the millscale surface. The corrosion rate increases as the dissolved carbon dioxide (CO2) concentration increases. Changes in the solution resistance, polarization resistance, and pore resistance are related to the corrosion kinetics and growth of the pores. The porous structure of the millscale increases the possibility of local separation of anodic and cathodic sites, which would promote localized corrosion at the base of pores. These stress-raising pits eventually could act as precursor sites for the initiation of SCC.
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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".