Cross-Contamination of Ignitable Liquid Residues on Wildfire Debris—Detection and Characterization in Matrices Commonly Encountered at Wildfire Scenes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ignitable liquid residue (ILR) samples play an important role in fire investigations. Similar to other types of forensic evidence, maintaining sample integrity depends on the prevention of cross-contamination during both storage and transport. This study examines cross-contamination in ILR samples on various sample matrices (gravel, soil, wood). After inducing leaks in a controlled environment, sample analysis by GC×GC-ToF MS allowed for sensitive detection and in-depth characterization of cross-contamination processes. The potential for false positive identification of ILR is notably present due to cross-contamination. Compound transmission for a mid-range ILR (gasoline), for instance, was detectable after a 1 h exposure, with a complete profile transfer occurring after 8 h regardless of the matrix type. Visual comparisons and uptake rate calculations further confirmed matrix interaction effects taking place in the form of inherent native compound interference and adsorbate–adsorbate interaction during transmission and extraction processes for soil and wood matrices. Chemometric analysis highlighted the advantage of employing statistical analysis when investigating samples under matrix interactions by identifying several statistically significant compounds for reliably differentiating cross-contamination from background and simulated positive samples in different volatility ranges and compound classes. Untargeted analysis tentatively identified three additional compounds of interest within compound classes not currently investigated in routine analysis. The resulting classification between background, contaminated, and simulated positive samples showed no potential for false positive ILR identification and improved false negative errors, as evidenced by classification confidences progressing from 88% for targeted and 93% for untargeted to 95% for a diagnostic ratio analysis of three ratios deployed in tandem.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it