Cani da cadavere: “dispositivo biologico specializzato” nell’individuazione di tracce ematiche latenti sulla scena del crimine. Da mito a prova scientifica.
Bibliographic record
Abstract
Aim. The canis lupus familiaris, due to his \nparticular olfactory characteristics, is used \nby the police to detect a wide range of substances \n(explosives, gunpowder, narcotics, \netc.). Trained dogs to the discovery and reporting \nhuman remains or not visible cadaveric \nblood, can be of great help. This study \nset out to investigate and validate with scientific \nmethod, a training protocol of dogs \nspecialized for research, tracking and reporting \nof cadaveric latent blood traces of blood. \nMethods. We used two Labrador Retriever. \nThe study was conducted for sixteen months, \nwith about 200 hours of simulation and 6240 \nsurveys, within a room suitably equipped. \nWe used blood of four patients who died due \nto trauma, collected in sterile and VOCs free \ntubes. The first phase of the training focused \non the ability of the two dogs to hold the \nsmell target and signal their presence at concentrations \nalways decreasing. In the second \nphase confounding factors were introduced. \nResults. The study found the real effectiveness \nof dogs trained to identify human cadaveric \nblood in very low concentrations. Tests \nconducted have shown a good ability to discriminate \nhuman cadaveric blood in combination \nwith confounding factors in high concentrations \n(olfactory accuracy). Conclusion. The use of dogs in this area necessarily \nrequires standardization of training procedures in order to achieve “certified” for \nthis specialized biological device the same \nrigorous level of reliability and reproducibility \nrequired for all methods of investigation in \nthe forensic field, through an optimized and \ntightly controlled training, through the evaluation \nof olfactory sensitivity, the ability of olfactory \ndiscrimination and olfactory accuracy.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".