Measuring Corrosion on Vehicles, in Real-Time, Using Digital Imaging and Analysis Techniques
Bibliographic record
Abstract
This research outlines a digital imaging method under development to systemize a rapid in-field corrosion evaluation measure, to evaluate and monitor the degree of corrosion on target corrosion-prone parts on light-duty vehicles. This procedure uses digital imaging to study and compare corrosion levels of 228 vehicles that were treated with aftermarket applications of corrosion prevention products versus 141 vehicles that were untreated. It introduces a Corrosion Index (CI) as a common measure. Single-factor and two-factor analysis of variance (ANOVA) of the digitally-based corrosion measurements show statistically significant correlations between CI and treatment (treated versus untreated), as well as CI, vehicle age, and treatment. The ANOVA results show that the aftermarket-treated vehicles have statistically significantly less corrosion than the untreated vehicles, demonstrating that digital image analysis is a viable method of measuring corrosion on corrosion-prone vehicle parts, offering the potential to monitor and track the performance/efficacy of aftermarket corrosion treatment in real-time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".