Some Directions of using the Forensic Odorology in the Field of the Fixing Evidences in the Pre-Trial Proceedings in Ukraine
Bibliographic record
Abstract
All over the world, the issue of detecting crimes committed is the main task of the state and its law enforcement agencies. The detection of crimes and the prosecution of those who committed them are in most cases based on evidence gathered by law enforcement officers. The issue of evidence is also quite debatable, because the legislation of different countries perceives different aspects of evidence and the evidence itself. The article covers the issue of individual issues related to obtaining evidence with the help of specially trained dogs. These forms and methods were used by Ukrainian law enforcement officials during their stay in the international peacekeeping mission in Kosovo. Currently, some evidence-gathering issues can be used in the process of investigating and prosecuting criminals with trained dogs. However, this area is not widely used, as there are a number of both practical and regulatory, as well as legislative problems. This direction in the activities of the police and other law enforcement agencies is called forensic odorology. The issue of using dogs in the process of detecting and investigating crimes is quite controversial and they are used differently in different countries.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".