## 4 Discussion and Conclusions  
BSM analysis remains to be a very important component of taphonomic studies, whereby their identification and in-depth analysis can reveal multiple components regarding early hominin populations [11; 12; 13; 14; 15], their development [6; 7; 23], and their associated paleoecologies [14; 24; 28]. Nevertheless, issues imposed by equifinality have led to complications in their identification and interpretation [1; 2; 3; 4; 5], requiring more objective and empirical methods that can be used for BSM classification and characterization [21; 22; 23; 24; 25; 26; 27; 30; 58]. In recent years, debates regarding the protocol used to identify cut marks have ranged from simple observational criteria [58] to developments with a more complex multivariate protocol [36] and advanced microscopic studies [59]. With the integration of ML to the processing of qualitative data, analysts have been able to improve the processing of archaeological data sets to a considerable degree [29]; nevertheless, the subjective nature upon which this data is obtained makes some of these advances debatable [32]. One alternative has been proposed utilizing DL convolutional neural network architectures for image processing and classification [30], presenting promising results for automated BSM identification. Here, we additionally present the combined usage of advanced digital microscopy, Geometric Morphometrics, and artificially intelligent computational algorithms for cut  
Figure 6: ROC curves with AUC values for Neural Network performance. (**A**) Worst recorded performance of the Neural Network model, obtaining an AUC of 0.9999. (**B**) best recorded performance of the Neural Network model, obtaining an AUC of 1. (**C**) Detail of dotted-square in (**A**), showing the slight perfection affecting this model’s achievement of a perfect right-angle “curve”. mark identification and characterization. On both accounts, ML and DL have effectively proven to outperform human performance [29; 30; 32], presenting a more objective and precise means of studying microscopic traces. The present results are able to develop data observed by multiple authors [12; 36; 37; 58], yet employing new empirical means of quantifying these conclusions. Geometric morphometric characterization of trampling and cut marks concur that the most significant features of cut marks are their depth and straight trajectory, while trampling marks are more variable presenting much more superficial morphologies alongside other irregularities [37]. Furthermore, the ability of high-resolution 3D digital microscopy to overcome limitations imposed by the superficial nature of some traces [37] can also be considered a significant improvement from previous efforts [21; 22]. While equifinality can still be observed to a certain degree, considering the high degree of overlap in most of these samples, it is important to point out the high dimensionality of PCA results derived from morphological data, as seen in how MANOVA testing is still able to identify significant differences between samples. Moreover, FFNN efficiently differentiates experimental samples with high levels of confidence on all accounts, considering their ability to extract complex patterns from difficult data [60]. The present study additionally complements previous efforts to implement ML algorithms in Geometric Morphometric analyses, expanding the available toolbox for morphological studies [61; 62; 63; 64; 65; 66]. [PERSON] et al. [26]'s original attempts to implement neural network architectures for tooth mark classification performed poorly, attributed by the authors to the model's superficial nature. The complexity of the model here supports this observation. These results thus confirm model configuration and tuning to be essential for efficient classification, requiring extensive experimentation to find the optimal model. This would also explain the mixed results obtained by similarly superficial models in applications for systematic biology [61; 62; 63; 64; 65; 66].