Forensic reconstruction of two military combat related shooting incidents using an anatomically correct synthetic skull with a surrogate skin/soft tissue layer
Bibliographic record
Abstract
Six synthetic head models wearing ballistic protective helmets were used to recreate two military combat-related shooting incidents (three per incident, designated 'Incident 1' and 'Incident 2'). Data on the events including engagement distances, weapon and ammunition types was collated by the Defence Science and Technology Laboratory. The models were shot with 7.62 × 39 mm ammunition downloaded to mean impact velocities of 581 m/s (SD 3.5 m/s) and 418 m/s (SD 8 m/s), respectively, to simulate the engagement distances. The damage to the models was assessed using CT imaging and dissection by a forensic pathologist experienced in reviewing military gunshot wounds. The helmets were examined by an MoD engineer experienced in ballistic incident analysis. Damage to the helmets was consistent with that seen in real incidents. Fracture patterns and CT imaging on two of the models for Incident 1 (a frontal impact) were congruent with the actual incident being modelled. The results for Incident 2 (a temporoparietal impact) produced realistic simulations of tangential gunshot injury but were less representative of the scenario being modelled. Other aspects of the wounds produced also exhibited differences. Further work is ongoing to develop the models for greater ballistic injury fidelity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".