Validating the Use of Amputated Limbs Used as Cadaver Detection Dog Training Aids
Bibliographic record
Abstract
Cadaver detection dogs (CDDs) are trained to locate human remains and/or objects associated with human remains. This is possible due to their extraordinary olfactory capabilities compared to those of humans. To reinforce this capability, CDDs must be trained and regularly exposed to the target odor in the form of training aids which include—chemical formulations, animal remains, and/or human remains. Currently, the Ontario Provincial Police (OPP) use amputated limbs/feet from consented surgeries performed on diabetic patients as cadaver detection dog training aids. There is limited knowledge about the volatile organic compound (VOC) composition of these training aids and their appropriateness as an alternative to human remains for CDD training purposes, which formed the aim of the current study. VOCs were collected from amputated lower limbs/feet repeatedly using thermal desorption (TD) tubes and analyzed with comprehensive two-dimensional gas chromatography—time-of-flight mass spectrometry (GC×GC-TOFMS). The response of cadaver detection dogs to these training aids was also recorded to understand their alert in the context of the detected VOCs. VOC classes including acids, alcohols, aldehydes, ketones, ester and analogues, ethers, aliphatic, cyclics, sulfur-containing, nitrogen-containing, and halogen-containing VOCs were identified. Of these classes, cyclic VOCs were most abundant followed by nitrogen-containing VOCs while ethers were the least abundant. The most prominent VOCs identified in amputated limbs/feet were decomposition related however, one VOC—sevoflurane, originated from anaesthesia during the surgeries. It was determined that the VOC profile of aged and relatively recent training aids were variable. The aged training aids sampled over time had less variability (compared to more recent training aids). Additionally, the VOC profiles of samples was not found variable owing to the storage conditions—room temperature, refrigerator or freezer. Overall, a 98.4% detection rate was observed for amputated limbs/feet used as CDD training aids and the presence of non-decomposition related VOCs such as sevoflurane did not appear to impact the CDDs’ detection capability. This study highlights that the presence of decomposition VOCs in amputated limbs/feet and their high detection rate by CDDs validates their use as alternative CDD training aids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".