A deep learning-based taphonomical approach to distinguish the modifying agent in the Late Pleistocene site of Toll Cave (Barcelona, Spain)

ABSTRACT

One of the most widely used methods to associate lithic tools and bone assemblage in archaeological sites is the identification of cut-marks. However, the identification of these marks is still problematic in some localities on account of the similarities with the modifications generated by non-human processes, including biostratinomic and post-depositional bone surface modifications. Toll Cave (Barcelona, Spain), with chronologies between 47.310 BP and 69.800 BP, is one of the case studies where the cut-marks could easily be confused with abundant grooves generated by the dragging of sedimentary particles (e.g. trampling), but also with the scores produced by carnivores. In this work, we present the results obtained from applying Deep Learning (DL) models to the taphonomic analysis of the site. This methodological approach has allowed us to distinguish the bone surface modifications with 97.5% reliability. We show the usefulness of this technique to help solve many taphonomic equifinality problems in the archaeological assemblages, as well as the need to implement new approaches to eliminate subjectivity in the descriptions of bone damage and make more accurate inferences about the past.

KEYWORDS:

• Deep Learning (DL)
• cut-marks
• scores
• trampling
• Toll Cave
• Late Pleistocene

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/08912963.2023.2242370.

Additional information

Funding

This research was funded by a research support contract within the RYC2019-026386-I project from the Spanish Ministry of Science and Innovation. The archaeological field-works and research at Toll Cave are funded by the project PID2019-103987GB-C31 and PID2022-138590NB-C41 from Spanish Ministry of Science and Innovation, and the projects CLT009/22/000045, 2021 SGR 01238 and 2021 SGR 01239 from the Generalitat de Catalunya (AGAUR). RB is supported by a Ramón y Cajal (RyC) research contract from the Ministry of Science and Innovation (RYC2019-026386-I). AR is a beneficiary of the Individual Call to Scientific Employment Stimulus—3rd Edition, promoted by the Portuguese FCT (reference: 2020.00877.CEECIND). She also develops her research within the research project PID2020-114462GB-I00, supported by the Spanish MICINN. The Institut Català de Paleoecologia Humana i Evolució Social (IPHES-CERCA) has received financial support from the Spanish Ministry of Science and Innovation through the ‘María de Maeztu’ program for Units of Excellence (CEX2019-000945-M). We thank the two anonymous reviewers for their constructive and insightful comments.