Recently the incorporation of artificial intelligence has allowed the development of valuable methodological advances in taphonomy. Some studies have achieved great precision in identifying the carnivore that produced tooth marks. Additionally, other works focused on human activity have managed to specify what type of tool or raw material was used in the filleting processes identified at the sites. Through the use of geometric morphometrics and machine learning techniques, the present study intends to analyze the cut marks of the Ulaca oppidum (Solosancho, Ávila, Spain) in order to identify the type of tools used during carcass modification. Although the Ulaca oppidum is an Iron Age site, the results suggest that most of the cut marks were produced with flint tools.

Taphonomic studies aim to identify the modifying agents that intervene in bone assemblages found at archaeopaleontological sites. Carnivores may modify, accumulate, or scavenge skeletal parts inflicting tooth marks, including scores, on the cortical surface. Several works have studied tooth score morphology to discern which carnivore group modified the bone assemblages, achieving different results. In the present study, different methods based on the use of landmarks and semilandmarks have been tested to describe and analyze the score profile cross-sections of spotted and brown hyenas, leopards, and lions. According to our results, the already published seven-landmark method is useful in order to differentiate between carnivore species from different families (e.g., felids and hyenids). Meanwhile, felid species (e.g., leopards and lions) cannot be consistently distinguished using any of the methods tested here. In contrast, hyenid species can be morphologically differentiated. On the other hand, the use of semilandmarks does not generally improve morphological characterization and distinction, but low numbers of landmarks and the inclusion of the score’s deepest point might provide the best results when semi-automatic semilandmark models are preferred to avoid sampling biases.