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Record W4406714273 · doi:10.32370/ia_2024_04_6

Forensic Research of Some Samples of Modern Foreign-Made Criminal Weapons in Wartime and the Most Common Mistakes

2025· article· en· W4406714273 on OpenAlexvenueno aff
Nataliia Pavlovska, Olha Nesen, Dina Rusanivska, Andrii Kofanov, Anzhela Tsypan

Bibliographic record

VenueIntellectual Archive · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyCriminal investigationLawPolitical scienceForensic engineeringPsychologyEngineering

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.043
GPT teacher head0.320
Teacher spread0.277 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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