Cross-Contamination of Ignitable Liquid Residues on Wildfire Debris—Effects of Packaging and Storage on Detection and Characterization
Bibliographic record
Abstract
Producing defensible data for legal proceedings requires strict monitoring of sample integrity. In fire debris analysis, various approved packaging and storage solutions are designed to achieve this by preventing cross-contamination. This study examines the efficiency of current practices at preventing cross-contamination in the presence of a sample matrix (charred wood) via analysis by comprehensive multidimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-ToF MS). The transfer of ignitable liquid residue (ILR) was assessed by comparing percentages of the target ILR area relative to the total chromatogram area and applying chemometric tools developed to detect cross-contamination. All practices reduced cross-contamination in comparison to faulty packaging. Individual practices varied in their performance. Nylon-based packaging performed best, whereas commercial polyethylene-based packaging performed worst due to interfering compounds emitted from the material and sealing mechanism. Heat-sealing was the best sealing mechanism when applied correctly, followed by press-fit connections, and lastly, adhesive sealing. Refrigerated storage offered several advantages, with elevated impact for polyethylene-based packaging and adhesive sealing mechanisms. Triple-layer packaging practices did not show significant benefits over double-layers. The recommended packaging approach based on these findings is mixed-material packaging (metal quart can in a heat-sealed nylon bag), offering advanced prevention of cross-contamination and practical advantages with continued refrigeration during transport.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".