Combining quantitative approaches to differentiate between backed products from discoidal and Levallois reduction sequences
EntityUAM. Departamento de Prehistoria y Arqueología
10.1016/j.jasrep.2022.103723Journal of Archaeological Science: Reports 46 (2022): 103723
ISSN2352-409X (print); 2352-4103 (online)
Funded byThis 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 Innovation
ProjectComunidad de Madrid. SI1/PJI/2019-00488; Gobierno de España. ID2019-103987GB-C33
SubjectsDeep learning; Discoid; Geometric morphometrics; Levallois; Lithic analysis; Machine learning; Arqueología; Historia
Rights© 2022 The Author(s)
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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
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