Ante- and post-mortem human volatiles for disaster search and rescue
Bibliographic record
Abstract
Ante-mortem metabolic processes are responsible for the release of volatile organic compounds, which form the primary component of human scent and are used by search-and-rescue canines in victim location efforts. Similarly, the post-mortem processes of autolysis and putrefaction produce malodourous compounds that cadaver detection dogs use to locate human remains. This review examines literature on ante-mortem and post-mortem volatiles, with a focus on studies from 2010 onwards. A total of 973 different compounds were reported over this period, from the live matrices blood (65), breath (124), fingernails (17), hair (24), saliva (343), skin (385), sweat (37), urine (80), the whole body (86), and unspecified sources (31), and during early decomposition (321), middle decomposition (49), late decomposition (102), and an unspecified timeframe (113). There are notably more studies examining the matrices from living volunteers than decedents, and methods vary significantly between studies on living and deceased individuals in sampling methodology and analytical instrumentation. To establish a profile that accurately reflects the whole human volatilome, the standardisation of methodology and further research are required. Determining the complete human odour profile will assist in victim location where living and deceased individuals are commingled (e.g. disaster sites), and will inform future technologies to aid in accelerating search-and-rescue operations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".