An Experimental Model of Tool Mark Striations by a Serrated Blade in Human Soft Tissues
Bibliographic record
Abstract
Tool mark analysis is a method of matching a weapon with the injury it caused. In a homicidal stabbing using a serrated knife, a stab wound that involves a cartilage may leave striations from the serration points on the blade edge. Assessing tissue striations is a means of identifying the weapon as having a serrated blade. This prospective study examines the possibility that similar striations may be produced in human soft tissues. Using tissues taken at the time of hospital-consented autopsies, stab wound tracks were assessed in a variety of human tissues (aorta, skin, liver, kidney, and cardiac and skeletal muscle). Stab wounds were produced postmortem with similar serrated and smooth-edged blades. The walls of the stab wounds were exposed, documented by photography and cast with dental impression material. Striations were identified by naked-eye examination in the skin and aorta. Photodocumentation of fresh tissue was best achieved in the aorta. Striations were not identified in wound tracks produced by the smooth-edged blade. Three blinded forensic pathologists were assessed for their ability to detect striations in photographs of wound tracks and had substantial interobserver agreement (κ = 0.76) identifying striations. This study demonstrates that tool mark striations can be present in some noncartilaginous human tissues.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".