Discrimination of 1990s Original Automotive Paint Systems: A Collaborative Study of Black Nonmetallic Base Coat/Clear Coat Finishes Using Infrared Spectroscopy
Bibliographic record
Abstract
The 1990s saw the introduction of significantly new types of paint binder chemistries into the automotive finish coat market. Considering the pronounced changes in the binders that can now be found in automotive paints and their potential use in a wide variety of finishes worldwide, the Paint Subgroup of the Scientific Working Group for Materials (SWGMAT) initiated a validation study to investigate the ability of commonly accepted methods of forensic paint examination to differentiate between these newer types of paints. Nine automotive paint systems typical of original equipment applications were acquired from General Motors Corporation in 1992. They consisted of steel panels coated with typical electrocoat primers and/or primer surfacers followed by a black nonmetallic base coat and clear coat. The primary purpose of this study was to evaluate the discrimination power of common forensic techniques when applied to the newer generation original automotive finishes. The second purpose was to evaluate interlaboratory reproducibility of automotive paint spectra collected on a variety of Fourier transform infrared (FT-IR) spectrometers and accessories normally used for forensic paint examinations. The results demonstrate that infrared spectroscopy is an effective tool for discriminating between the major automotive paint manufacturers' formulation types which are currently used in original finishes. Furthermore, and equally important, the results illustrate that the mid-infrared spectra of these finishes are generally quite reproducible even when comparing data from different laboratories, commercial FT-IR instruments, and accessories in a "real world," mostly uncontrolled, environment.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".