Volatile organic compounds of diesel and porcine bone in a simulated controlled fire
Bibliographic record
Abstract
The detection of burned human remains in a fire is daunting, mainly when identifiable skeletons are not found. This study aims to identify volatile organic compounds (VOC) released from the burning of porcine bones in the presence of diesel in a simulated controlled outdoor setting in Malaysia. Neat diesel was diluted with hexane with a ratio of 1:1 and administered into a gas chromatography-mass spectrometry (GC-MS). Porcine bone was burned to identify VOCs of porcine bones, whereas 30 mL diesel was burned together with porcine bones to identify VOCs produced from the combined burning. After the burning process, an activated carbon tablet was fixed to the burned sample. Later, the tablet was desorbed with hexane and analysed using GC-MS. Results revealed that the combined burning released a set of VOCs that were not detected in burned porcine bone or neat diesel. This work was able to enforce the detection of specific volatiles from various functional groups such as alkanes, isoalkanes, alkylbenzenes and ketones in the combined burning of diesel with porcine bones. It was also discovered that in the specific conditions applied and controlled in this study, most VOCs of porcine bone and diesel respectively were not detected in the combined burning of porcine bone and diesel.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".