Literature DB >> 27274806

Cut marks on bone surfaces: influences on variation in the form of traces of ancient behaviour.

David R Braun1, Michael Pante2, William Archer3.   

Abstract

Although we know that our lineage has been producing sharp-edged tools for over 2.6 Myr, our knowledge of what they were doing with these tools is far less complete. Studies of these sharp-edged stone tools show that they were most probably used as cutting implements. However, the only substantial evidence of this is the presence of cut marks on the bones of animals found in association with stone tools in ancient deposits. Numerous studies have aimed to quantify the frequency and placement of these marks. At present there is little consensus on the meaning of these marks and how the frequency relates to specific behaviours in the past. Here we investigate the possibility that mechanical properties associated with edges of stone tools as well as the properties of bones themselves may contribute to the overall morphology of these marks and ultimately their placement in the archaeological record. Standardized tests of rock mechanics (Young's modulus and Vickers hardness) indicate that the hardness of tool edges significantly affects cut-mark morphology. In addition, we show that indentation hardness of bones also impacts the overall morphology of cut marks. Our results show that rock type and bone portions influence the shape and prevalence of cut marks on animal bones.

Entities:  

Keywords:  Palaeolithic; cut marks; diet; mechanical properties

Year:  2016        PMID: 27274806      PMCID: PMC4843629          DOI: 10.1098/rsfs.2016.0006

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


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7.  2.5-million-year-old stone tools from Gona, Ethiopia.

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10.  Investigating the signature of aquatic resource use within Pleistocene hominin dietary adaptations.

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2.  Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks.

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