Cadaver‐detection dogs: A review of their capabilities and the volatile organic compound profile of their associated training aids
Bibliographic record
Abstract
Abstract Cadaver‐detection dogs (CDDs) are an essential tool for the search and detection of human remains. In order to enhance their search capability, CDDs are regularly trained on natural and synthetic training aids. The odor profile of these training aids comprises a range of volatile organic compounds (VOCs) which is intended to resemble those produced by a decomposing body. It is currently unknown if detector dogs respond to the same stimuli and whether it is a specific VOC or a suite of decomposition‐related VOCs as their target odor. This review summarizes the VOCs that have been detected in various CDD training aids such as blood, human remains, decomposition fluid, soil, buried remains, textile, and synthetic formulations. Additionally, it discusses the reported capability of CDDs to respond to each of these training aids. The purpose of this review is to understand the variability of VOCs in CDD training aids and the response of CDDs to this wide range of compounds. Additionally, this review attempts to determine if there is a specific training aid to which CDDs respond preferentially. Such a review will assist to establish better practices for CDD training since no standardized practices exist globally. This article is categorized under: Crime Scene Investigation > Special Situations and Investigations Forensic Anthropology > Taphonomic Changes and the Environment Forensic Medicine > Death Scene Investigation
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".