This study explores the performance of machine learning algorithms on the classification of fossil teeth in the Family Bovidae. Isolated bovid teeth are typically the most common fossils found in southern Africa and they often constitute the basis for paleoenvironmental reconstructions. Taxonomic identification of fossil bovid teeth, however, is often imprecise and subjective. Using modern teeth with known taxons, machine learning algorithms can be trained to classify fossils.
Gregory J. Matthews, George K. Thiruvathukal, Maxwell P. Luetkemeier, and Juliet K. Brophy, Examining the use of Amazon’s Mechanical Turk for edge extraction of the occlusal surface of fossilized bovid teeth, PLoS ONE, https://doi.org/10.1371/journal.pone.0179757
Gregory J. Matthews, Juliet K. Brophy, Matthew P. Luetkemeier, Hongie Gu, and George K. Thiruvathukal, A comparison of machine learning techniques for taxonomic classification of teeth from the Family Bovidae, Journal of Applied Statistics (2018), https://doi.org/10.1080/02664763.2018.1441381
Juliet K. Brophy, Gregory J. Matthews, George K. Thiruvathukal. An analysis of the effect of tooth wear on bovid identification. S Afr J Sci. 2019;115(7/8), Art. #5496, 5 pages. https://doi.org/10.17159/sajs.2019/5496