Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12136/1392
Item metadata
Title: Classifying agency in bone breakage: an experimental analysis of fracture planes to differentiate between hominin and carnivore dynamic and static loading using machine learning (ML) algorithms
Authors: Moclán Ramos, Abel
Domínguez-Rodrigo, Manuel
Yravedra Saínz de los Terreros, José
Keywords: Taphonomy;Machine learning;Algorithm;Bone breakage;Fracture planes
Issue Date: Sep-2019
Publisher: Springer
Citation: Archaeological and Anthropological Sciences, 2019, 11 (9), 4663-4680
Abstract: The analysis of bone breakage has always been underrepresented in taphonomic studies. Analysts, thus, lose the opportunity to resolve an important part of the equifinality related to activities that hominins and different types of carnivores may produce. Recent studies have shown that the use of powerful machine learning (ML) algorithms allow the accurate classification of bone surface modifications (BSM). Here, we present an experimental study, applying these algorithms to the analysis of bone breakage patterns. This statistical methodology allows the correct classification of three different assemblages which have been generated anthropogenically and by the activity of carnivores (i.e., hyenas and wolves). ML algorithms applied to a multivariate set of properties of broken bone specimens yielded an accuracy of 95% and were higher in classifying agency without the need to include information from BSM. This paper proposes a methodological approach that opens the door to improve our understanding of referential frameworks regarding bone breakage and to determine agency in prehistoric bone breakage processes.
URI: http://hdl.handle.net/20.500.12136/1392
ISSN: 1866-9557
1866-9565
DOI: 10.1007/s12520-019-00815-6
Editor version: https://doi.org/10.1007/s12520-019-00815-6
Type: Article
Appears in Collections:Arqueología



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.