Macroscopic Chop Mark Identification on Archaeological Bone: An Experimental Study of Chipped Stone, Ground Stone, Copper, and Bronze Axe Heads on Bone
Bibliographic record
Abstract
This paper presents a new macroscopic method for identifying chop marks on archaeological faunal assemblages and highlights the major differences in the morphology of chop marks created by stone and metal axes. The method provides macroscopic criteria that aid in the identification of both complete and incomplete chop mark types as well as the raw material of the axe. Experiments with modern stone (chipped and ground) and metal (copper and bronze) axes found that the degree of fragmentation within a chop mark is related to both the width and sharpness of the axe and can be classed on a scale from 1–5 using a variety of criteria. The experiments demonstrate that sharp chipped stone axes are fragile (often break upon impact) and do not create clean and well-defined chop marks. Ground stone axes are more durable but tend to create very fragmented chop marks without a clean cut (sheared) surface. Unalloyed copper metal axes can create sheared chopped surfaces; however, the relatively soft metal creates more crushing at the point of entry than bronze axes. In contrast, bronze axes are durable and create chop marks with exceptionally low rates of fragmentation resulting in a clean-cut sheared surface that extends into the bone for more than 3 mm. The method is applied to the faunal assemblage from the Early Bronze Age site of Göltepe, Turkey to determine whether the chop marks on bones were made by stone or metal axes at this early metal processing settlement. The results suggest that many of the chop marks were made by metal implements (e.g., axes). Hence, this method provides another means to monitor the adoption rates of new raw materials at a time when both metal and stone axes coexisted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".