The comparison of volatile organic compound profiles between human and non-human bones and its application to human remains detection dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Human remains detection dogs can distinguish human and non-human remains. • Odor profiles from human and non-human bones are different. • Human and deer bones demonstrated the most complex VOC profiles. Human Remains Detection (HRD) dogs are specifically trained to aid law enforcement agencies in search operations for deceased victims. Their olfactory sensitivity and specificity highlight the importance of choosing target odor sources for HRD training. While HRD dogs rely on olfactory cues to locate human remains, it is important to identify which volatile organic compounds (VOCs) they are alerting to among those released during the various stages of the human decomposition process. In this study, VOC profiles from human and non-human bones were collected and analyzed using thermal desorption coupled to comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (TD-GC × GC-TOFMS). The non-human decomposition VOC profiles were compared to human VOC profiles obtained from sections of amputated human limbs used as HRD training aids. These limb sections were previously decomposed to the dry remains/skeletonization stage. The olfactory responses of HRD dogs in the presence of these training aids and non-human remains were subsequently investigated with results demonstrating their capability in distinguishing human from non-human remains. Highlighting the differences in VOC profiles between human and non-human decomposition may help to enhance the sensitivity of HRD dogs to human remains while recognizing the importance of using human cadaveric material for training purposes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it