Development of a Model for Experimental Data Treatment of Diffusion and Activation Limited Polarization Curves for Magnesium and Steel Alloys
Bibliographic record
Abstract
Potentiodynamic polarization curves are often performed to characterize the bulk corrosion properties of a material in terms of its corrosion potential, rate, and Tafel kinetics in a given environment. This requires approaching them as one of two extreme cases, with purely activation or diffusion controlling the measured currents. For the intermediate behavior displayed by many real samples, making this assumption is a significant source of error. In this work, a finite element model has been developed which considers both of these contributions simultaneously for experimental data with a broad material scope. This has been used to examine both extremely corrosion vulnerable samples in two magnesium alloys and extremely corrosion resistant samples in two steels. The effectiveness of several goodness of fit statistics in comparing the similarity between experimental and simulated data has also been assessed.
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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".