Forensic Research of Some Samples of Modern Foreign-Made Criminal Weapons in Wartime and the Most Common Mistakes
Bibliographic record
Abstract
The article discusses some aspects of the preliminary forensic and forensic research of individual samples of modern weapons produced in Europe and America. Special attention is paid to samples of smoothbore firearms. It is the most widespread but clearly underestimated from the point of view of forensics. Crimes committed during the war with the use of smoothbore weapons are the most serious in terms of consequences. Smoothbore weapons are more destructive and powerful compared to rifled weapons at close range. The article proposes a new forensic (modern, creative) author's classification of foreign-made smoothbore weapons. Particular attention is paid to the most widespread forensic and research errors in weapons research and ways to solve them during the war. The short historical analysis of appearing, development and formation of smooth-bore firearms in the world allows to pass to us to definition of concept and its classification on fighting, those of special purpose and the hunting smooth-bore firearms.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".