The Characterization of Automobile Body Fillers*
Bibliographic record
Abstract
Body fillers are sometimes encountered with paint evidence from hit-and-run accidents. Little forensic research has been conducted and published on the subject since 1986. The objective of this study was to determine if chemical and physical differences in body fillers from various manufacturers existed and could be identified. Thirty-three samples of light-weight automobile body fillers and spot putties were obtained. The fillers and putties were compared using light microscopy, infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray spectrometry (SEM-EDX), and pyrolysis gas chromatography (pyGC). Results from fourier transform infrared spectroscopy analysis placed the samples into five groups and differentiated six samples. Light microscopy placed the samples into one of five color groups. PyGC placed the samples into three groups and differentiated one sample. SEM-EDX placed the samples into four groups and differentiated 13 samples. Using these analysis methods, 19 of the 33 samples could be discriminated. The best discriminatory tool was found to be SEM-EDX.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".