Examination of Hacking and Blunt Force Skeletal Trauma
Bibliographic record
Abstract
Hacking trauma is prevalent in forensic cases involving genocide and dismemberment, but research into the identification of this type of trauma is lacking. The present study examines characteristics of hacking and blunt force skeletal trauma in order to determine if there is a point at which blunt force trauma becomes distinguishable from hacking trauma. Ten implements with a range of blade angles (i.e., the striking surface of the implement) were used in conjunction with a controlled-force hacking device to impact 100 limb bones of white-tailed deer (Odocoileus virginianus). Observations of the trauma included the occurrence and degree of fragmentation, the entrance widths of the impacts, and composite scores of six hacking characteristics, especially the distinctive V-shaped kerf. ANOVA tests and regression analyses were used to assess the relationships between these characteristics and the blade angles. A significant relationship (p-value = 0.011) was found between the composite hacking scores and the blade angles, indicating that blunt force and hacking trauma can be distinguished. The entrance widths of the impacts exhibited a significant relationship with the blade angles (p-value = 0.037). There was also a significant relationship between the visibility of a V-shaped kerf in the bones (p-value = 0.003), with visibility decreasing around the 60° blade angle. These data should assist in establishing guidelines to differentiate hacking and blunt force skeletal trauma in cases where the implement is on a spectrum between sharp and blunt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".