Identifying VOCs from human remains detectable in water using comprehensive two-dimensional gas chromatography
Bibliographic record
Abstract
Understanding the volatile organic compounds (VOCs) emitted during human decomposition is crucial for search and recovery investigations and the development of improved human remains detection methods. However, the influence of water on human decomposition, and particularly the release of VOCs has received minimal attention compared to terrestrial scenarios. This knowledge gap is highly relevant for training human remains detection (HRD) dogs, as they are deployed in various scenarios, including land and water searches, yet little is known about the VOC profiles produced by human remains in these different environments. The aim of this study was to establish a proof-of-concept methodology for collecting VOCs from submerged remains. Sorbent tubes and thin-film solid phase microextraction (TF-SPME) were utilized as neither have been studied for this purpose previously. Human remains were submerged in a tank of water. Headspace samples were collected by placing a metal hood over the tank to trap VOCs , which were then drawn into a sorbent tube via an air sampling pump. Water samples were collected for direct immersion utilizing TF-SPME membranes in the laboratory. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry combined with thermal desorption was employed to analyze both sample types. The sorbent tube method identified 42 compounds while the TF-SPME technique identified 34 compounds. Overall, this study successfully demonstrated the feasibility of both VOC collection and analysis methods for human remains decomposition in water.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".