Prediction for CO2 Corrosion of Active Steel under a Precipitate
Bibliographic record
Abstract
Abstract This paper presents a convenient deterministic CO2 corrosion model which encapsulates CO2 dissolution, hydration, diffusion, H2CO3 formation and dissociation, local ionic interactions, FeCO3 precipitation and electrochemical (anodic and cathodic) reactions at the steel surface. Good agreement between the model results and a variety of published experimental data is shown under both FeCO3- saturated and unsaturated solution boundary layers. From a theoretical point of view and in a quantitative manner, the model revealed that the steel corrosion in carbonic acid is more severe than in hydrochloric acid for the same pH, due to H2CO3 reduction. Also shown is that as temperature increases, the corrosion rate increases substantially. This model allows for a reliable CO2 corrosion prediction for steels being used in petroleum production and gas transportation systems. This model in its current form is not fully applicable for steel surfaces either dry or covered with a real passive scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".