Establishing the volatile organic compound profile and detection capabilities of human remain detection dogs to human bones
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
The detection of skeletal remains using human remain detection dogs (HRD) is often reported anecdotally by handlers to be a challenge. Limited studies have been conducted to determine the volatile organic compounds (VOCs) emitted from bones, particularly when there is limited organic matter remaining. This study aimed to determine the VOCs emitted from dry, weathered bones and examine the detection performance of HRD dogs on these bones when used as training aids. The VOCs of four different bones (clavicle, rib, humerus, and vertebrae) from three cadavers were collected using sorbent tubes and analyzed using comprehensive two-dimensional gas chromatography‒time-of-flight mass spectrometry (GC × GC‒TOFMS). Subsequently, the responses of the HRD dogs to the bone samples were recorded over two separate two-day trials. A total of 296 VOCs were detected and classified into chemical classes, with aromatics and linear aliphatics being the most abundant classes. Several differences in the chemical class distribution were observed between the bone types, but the number and intensity of the VOCs were similar between the bone samples. During the HRD dog training, a higher false detection rate was observed on the first day of each trial; however, the detection rate improved to 100 % on the second day of each trial. Although the dogs are capable of detecting bones, they require exposure to and training with a diverse range of skeletal remains to enhance their efficiency. This is necessary due to the variations in the types and intensity of VOCs compared to earlier decomposition stages involving soft tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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