Open AccessDistinguishing Discoid and Centripetal Levallois methods through machine learning
Author(s) -
Irene González-Molina,
Blanca Jiménez-García,
JoséManuel MaílloFernández,
Enrique Baquedano,
Manuel Domı́nguez-Rodrigo
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0244288
Subject(s) - centripetal force , knapping , algorithm , artificial intelligence , computer science , dorsum , mathematics , archaeology , anatomy , biology , physics , geography , mechanics
In this paper, we apply Machine Learning (ML) algorithms to study the differences between Discoid and Centripetal Levallois methods. For this purpose, we have used experimentally knapped flint flakes, measuring several parameters that have been analyzed by seven ML algorithms. From these analyses, it has been possible to demonstrate the existence of statistically significant differences between Discoid products and Centripetal Levallois products, thus contributing with new data and a new method to this traditional debate. The new approach enabled differentiating the blanks created by both knapping methods with an accuracy >80% using only ten typometric variables. The most relevant variables were maximum length, width to the 25%, 50% and 75% of the flake length, external and internal platform angles, maximum width and number of dorsal scars. This study also demonstrates the advantages of the application of multivariate ML methods to lithic studies.