Identifying the transition from ante-mortem to post-mortem odor in cadavers in an outdoor environment
Bibliographic record
Abstract
This study investigates the transition from ante-mortem to post-mortem odor in human remains during the early post-mortem period in an outdoor environment. Three cadavers (donors) were placed at an outdoor human decomposition facility, and volatile organic compounds (VOCs) were collected and analyzed using thermal desorption coupled with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (TD-GC × GC-TOFMS). The key findings revealed that nitrogen-containing compounds were predominant in early post-mortem VOC profiles, driven by enzymatic and bacterial activity. Esters, alcohols, and halogenated compounds were also identified, with esters linked to microbial transformation and alcohols possibly formed by lipid peroxidation. Ante-mortem VOCs were persistent across samples, influenced by skin microbiota and environmental factors like UV radiation, complicating the detection of decomposition odor. Post-mortem VOCs became more prominent after ADD 73.4(experimental day 3), signaling the transition to the bloat stage of decomposition. Variations in sample collection methods and external factors such as temperature were found to affect VOC abundances. This study provides critical insights into odor transition and has implications for the use of search and rescue (SAR) and human remains detection (HRD) dogs. Further research is needed to standardize methods and assess odor transitions across diverse environments and seasons.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".