Experiment-Supported Model Development for Data Treatment of Diffusion and Activation Limited Polarization Curves of Magnesium and Steel Alloys
Bibliographic record
Abstract
Characterization of any corroding system begins with determining its corrosion potential and rate. These two values serve as a preliminary measure of its surface passivation and kinetic activity which may then be investigated in more detail using local electrochemical or spectroscopic techniques. The potentiodynamic polarization curve (PDP) is the most common technique for simultaneous extraction of these two values, since it provides additional kinetic information in the form of Tafel slopes and can further be used to measure the pitting potential of a system. In the past, numerical analysis of these curves has proven challenging where mass transport limitations influence the currents measured. In this presentation we discuss a finite element model that has been developed to analyse the kinetics of corroding magnesium and steel alloys during PDP’s where both activation and diffusion-controlled currents are present. Furthermore, the origins of the mass transport limitations present in these systems have been investigated in more detail through an analysis of the concentration profiles involved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".