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Record W4285083359 · doi:10.3389/frans.2022.934639

Validating the Use of Amputated Limbs Used as Cadaver Detection Dog Training Aids

2022· article· en· W4285083359 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFrontiers in Analytical Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsTrent UniversityGovernment of OntarioUniversité du Québec à Trois-Rivières
FundersFondation de l’UQTRNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité du Québec à Trois-Rivières
KeywordsOdorContext (archaeology)CadaverMedicineChromatographyChemistryEnvironmental chemistrySurgeryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.264
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it