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Record W3185595650 · doi:10.1111/1556-4029.14814

Shotgun slug wads as a marker of range of fire: A case report and novel firearm testing data

2021· article· en· W3185595650 on OpenAlexaff
Michael Multan, Shannon Moore, Éloïse Forest‐Allard, Matthew M. Orde

Bibliographic record

VenueJournal of Forensic Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsBC Innovation CouncilUniversity of British Columbia
Fundersnot available
KeywordsShotgunSlugRange (aeronautics)Poison controlForensic engineeringAccidentalMedicineEngineeringMedical emergencyChemistryPhysics

Abstract

fetched live from OpenAlex

Assessment of wound characteristics and the identification of various constituents of firearm discharge at autopsy play a key role in the determination of range of fire. In relation to wounds caused by shotguns, identification of the wad within the wound track, or of injury caused by the wad, is typically thought to suggest a fairly close range of fire. We present a case of a fatality due to a shotgun slug wound where the presence of the wad within the decedent's body was proposed by defense at criminal trial to favor accidental close range discharge during a struggle for the weapon-as opposed to the prosecution's contention of intentional firing of the weapon from a greater range and through an intermediate target. We undertook test firing of a shotgun of similar design to that which was fired during the interaction (a 12-gauge pump-action shotgun) using shotshells consistent with the slug that was recovered from the body (Winchester Super X brand), which demonstrated that the non-attached fiber wad present in this shotshell design can accompany the slug over distances of at least up to 22 feet (6.7 m) and even after transit through intermediate targets such as a vehicle headrest. These novel data provide assistance with estimation of range of fire in instances of injuries caused by shotgun slugs.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.110
GPT teacher head0.353
Teacher spread0.243 · 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 designCase report
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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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