Passivation/immersion method to grow pits in pipeline steel and a study of pit nucleation and growth resulting from the method
Bibliographic record
Abstract
A newly developed passivation/acid immersion technique was employed to produce pits free from deformation or residual stress on X-52 pipeline steel samples. Pits have been found to be a common source of crack initiation and the experimental procedure would prove useful in research in crack initiation. Pits generated using this technique were approximately hemispherical. It was seen that individual pits increased in radius at a linear rate of 0·33 μm h−1 and depth at a linear rate of 0·39 μm h−1. In the early stage of the process, single pit nucleation was the dominant process, and the area covered by pits increased to 1·7% in the first 40 h, and 75% of the pits were individual pits. In contrast, pit coalescence became significant in the later stages, and by 120 h the area coverage was up to 13·5% and only 50% of the pits were individual pits. The linked pits contained an increasing number of individual pits as time progressed containing up to five pits after 120 h and the linked pits tended to become more circular with time. The growth behaviour of linked pits was significantly different from individual pits. This study establishes some of the details of how pits nucleate, grow and link together. The published literature showed that the growth exponent of pits varied with the severity of the corrosion environment and the growth law determined here was at the high end of the published growth exponents.
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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".