Evaluation of the Permanence of Flame Accelerating Material on Burned Material with GC-MS Analytical Techniques
Bibliographic record
Abstract
The aim of the work is to evaluate the permanence, understood as a presence detected by the analyzes, after a burn, of the flame-accelerating material used to start a fire, even in largely burnt residues, in order to be able to recognize, even in the investigative context, whether the fire was caused by an accident or if it was facilitated by the emission (voluntary or non- voluntary) of a flame-accelerating substance. The analytical technique used to give answers to this is the use of the gas chromatograph-mass spectrometer (GC-MS), which allows to detect the presence of flame accelerators even in small quantities in the samples. A series of burnt material samples will be reproduced in a controlled environment, originally containing flame-accelerating materials and then compared to burnt samples without flame-accelerating agents. The results will be obtained through GC-MS analysis with the headspace (HS) and fiber (SPME) method, in order to evaluate, in greater detail, which of the two methods is the best performing to achieve the objective. As final result, the SPME fiber is very performing for the goal to be achieved, because it is able to detail the flame accelerating compounds found.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".