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Peak Penetration Force during Stabbing of Chest Wall with a CeremonialSword

2024· article· en· W4391145463 on OpenAlexaff
Geoffrey T. Desmoulin, Marc-André Nolette, Theodore E. Milner

Bibliographic record

VenueCurrent Forensic Science · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsMcGill University
Fundersnot available
KeywordsSWORDPenetration (warfare)Materials scienceMedicineArtComposite materialEngineeringMechanical engineeringOperations research

Abstract

fetched live from OpenAlex

Background: The force required for a sword to penetrate the human chest was identified as an important issue for the defense in a case of homicide by stabbing. Previous literature on penetration force had tested knives but not swords. Objective: The objective of the current study was to determine the peak force during penetration of a surrogate for human tissue with a ceremonial sword. Methods: The sword was secured to an MK-10 Tensile Tester and forced to penetrate a pork rib cut at speeds of 350 mm/min and 1100 mm/min, including both regions of rib and cartilage for pork ribs without skin or covered with a layer of porcine skin. Results: In the case of the pork ribs without skin, the mean peak penetration force at a speed of 350 mm/min was 11.0 N compared to a mean of 10.5 N at a speed of 1100 mm/min. The distributions of peak penetration forces at the two speeds were not significantly different. In the case of the pork ribs covered with porcine skin, the mean peak penetration force at a speed of 350 mm/min was 50.0 N compared to a mean of 47.6 N at a speed of 1100 mm/min. The distributions of peak penetration forces at the two speeds were again not significantly different. Conclusion: Forces of less than 50 N would be required for a ceremonial sword to penetrate the tissues of the human chest, although there is a risk of penetration for forces as low as 5 N when the effect of the porcine skin is not considered. Furthermore, the force required for penetration did not vary significantly over a three-fold speed of penetration.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.299
Teacher spread0.276 · 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
Published2024
Admission routes1
Has abstractyes

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