Combining quantitative approaches to differentiate between backed products from discoidal and Levallois reduction sequences
Entity
UAM. Departamento de Prehistoria y ArqueologíaPublisher
ElsevierDate
2022-11-05Citation
10.1016/j.jasrep.2022.103723
Journal of Archaeological Science: Reports 46 (2022): 103723
ISSN
2352-409X (print); 2352-4103 (online)DOI
10.1016/j.jasrep.2022.103723Funded by
This research has been supported by the project SI1/PJI/2019-00488 funded by Comunidad Autonoma de Madrid and Universidad Autonoma de Madrid. FR research studies are also supported by the project ID2019-103987GBC33 funded by the Spanish Ministry of Science and InnovationProject
Comunidad de Madrid. SI1/PJI/2019-00488; Gobierno de España. ID2019-103987GB-C33Editor's Version
https://doi.org/10.1016/j.jasrep.2022.103723Subjects
Deep learning; Discoid; Geometric morphometrics; Levallois; Lithic analysis; Machine learning; Arqueología; HistoriaRights
© 2022 The Author(s)Abstract
Backed flakes (core edge flakes and pseudo-Levallois points) represent special products of Middle Paleolithic
centripetal flaking strategies. Their peculiarities are due to their roles as both a technological objective and in the
management of core convexities to retain its geometric properties during reduction. In Middle Paleolithic contexts, these backed implements are commonly produced during Levallois and discoidal reduction sequences.
Backed products from Levallois and discoidal reduction sequences often show common geometric and
morphological features that complicate their attribution to one of these methods. This study examines the
identification of experimentally produced discoidal and recurrent centripetal Levallois backed products
(including all stages of reduction) based on their morphological features. 3D geometric morphometrics are
employed to quantify morphological variability among the experimental sample. Dimensionality reduction
though principal component analysis is combined with 11 machine learning models for the identification of
knapping methods. A supported vector machine with polynomial kernel has been identified as the best model
(with a general accuracy of 0.76 and an area under the curve [AUC] of 0.8). This indicates that combining
geometric morphometrics, principal component analysis, and machine learning models succeeds in capturing the
morphological differences of backed products according to the knapping method
Files in this item
Google Scholar:Bustos Pérez, Guillermo
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Gravina, Brad
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Brenet, Michel
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Romagnoli, Francesca
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