Detection of Petrol (Gasoline) in Fire Debris by Gas Chromatography/Mass Spectrometry/Mass Spectrometry (GC/MS/MS)
Bibliographic record
Abstract
Abstract Gas chromatography/mass spectrometry (GC/MS) is a well-known instrumental technique used for the analysis of fire debris for accelerant detection. However, matrix problems, such as pyrolysis product interference, are still encountered. These interferences often lead to inconclusive interpretation of the chromatographic results obtained. This paper describes a method of analyzing arson accelerants using gas chromatography coupled with ion trap mass spectrometry/mass spectrometry (GC/MS/MS). Petrol was investigated as an accelerant. Analysis incorporated an ion preparation method (IPM) that manipulates the ion population in the ion trap following ionization but prior to ion analysis. Parent ions of selected mass-to-charge (m/z) values, characteristic for petrol, are stored within the ion trap. All other unwanted ions are physically removed from the trap by sophisticated electrostatic waveforms. The parent ions fill the trap and are then fragmented by means of collision-induced dissociation (CID). The resultant daughter ion spectra are obtained, which can be used for identification purposes. In this manner, ions of pyrolysis origin are removed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".