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Record W4412561734 · doi:10.1504/ijfe.2025.147537

Puncturing a castle defence: injury biomechanics solution to a homicide investigation case study

2025· article· en· W4412561734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Forensic Engineering · 2025
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsMAB-Mackay Rehabilitation Centre
Fundersnot available
KeywordsHomicideBiomechanicsPuncturingPoison controlForensic engineeringAeronauticsEngineeringInjury preventionMedicineMedical emergencyAnatomy

Abstract

fetched live from OpenAlex

In the wake of a homicide, investigators were confronted with a castle doctrine self-defence argument that was difficult to refute with the tools and evidence at their immediate disposal. However, in his confession, the suspect claimed to have caused the stab wounds in an unusual manner. The victim's autopsy revealed that one of the stab wounds pierced the sternum and cut into the victim's heart, while another cut between ribs and stopped only at the knife hilt, causing a rib fracture. To provide insight into the likelihood of the suspect's narrative, investigators turned to injury biomechanics. It was possible to show the suspect's version of events had a low to an impossible chance of occurring through quantitative testing relating to sternum stabbing, rib fracture load, and ergonomic analysis, ultimately leading to a change in plea to 3rd-degree murder. This case illustrates the worth of injury biomechanics when dealing with complex homicides and why the standard investigative toolbox should include it.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.311
Teacher spread0.296 · 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