Sharp Force Trauma and Chop Mark Identification Bias: Experimental Evidence on the Effects of Bone Morphology, Cortical Thickness, and Ax Material
Bibliographic record
Abstract
ABSTRACT Sharp force trauma (SFT) is the main criterion used to identify chop mark butchery in zooarchaeology, yet its reliability as a diagnostic feature has not been systematically tested. Chop marks reflect both cutting and fracturing processes and exhibit characteristics of both sharp and blunt trauma. When present, SFT creates a distinct anthropogenic surface that is easily recognizable, whereas chops lacking SFT can resemble general fracture surfaces. This study investigates the potential bias within chop mark analysis by testing how bone type, cortical thickness, and ax material influence the presence and magnitude of SFT. Medium‐sized mammal femora and cervical vertebrae were impacted using stone, copper, bronze, iron, and modern steel ax heads under controlled energy conditions using the Instron 9440 Drop Tower System. Results modeled with a Bayesian hurdle‐lognormal framework show that bone morphology and tool material jointly determine SFT formation and depth. Iron and steel axes generated SFT more often and with greater depth than stone or copper axes, while femora produced significantly less visible SFT than vertebrae. Reliance on SFT as the main diagnostic criterion introduces material and mechanical bias, obscuring evidence of certain tool types and distorting interpretations of butchery behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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".