The Effects of Chloride Droplet Properties on the Underoil Corrosion of API X100 Pipeline Steel
Bibliographic record
Abstract
The corrosive environment expected to form in diluted bitumen pipelines was explored by simulated exposure with a paraffin oil-covered chloride droplet on API X100 pipeline steel. The effects of droplet volume, chloride ion concentration, temperature, initial pH, and cation species on the underoil droplet corrosion behavior of API X100 pipeline steel were studied by corrosion morphology and product identification combined with corrosion penetration measurements. The corrosion rate in the active region beneath the oil-covered sodium chloride droplets was inversely proportional to droplet volume but increased with increasing temperature and chloride ion concentration. Corrosion attack morphology was found to be dependent on initial droplet pH. At pH 2, uniform corrosion occurred across the entire area exposed under the oil-covered droplet. The oil-covered sodium chloride droplets with initial pH of 4 accelerated the uniform corrosion when compared to the droplet without initial pH control (pH ∼ 5.5). However, at a high initial pH of 10, two active regions displaying different general corrosion rates and one inactive region were observed under the oil-covered droplet. At an even higher initial pH of 12, no obvious uniform corrosion was observed beneath the oil-covered droplet. Finally, in the exposures to droplets with varied cation, the uniform corrosion in the active region was reduced by either calcium or magnesium ions in the oil-covered droplet.
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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".