## 1 Introduction  
The publication of the 'oldest' anthropic evidence of any type is always a problematic issue, usually drawing attention, criticism, and eventual debate on the quality of these findings from the entire archeological and paleontological community. Perfect examples of such debates can be observed in the claims of \(\sim\)3.4 Ma cut marks from Dikika, Ethiopia [1], which have since been heavily criticized and rejected [2,3]. Likewise, sites claiming to have \(\sim\)2.6 Ma cut marks outside of Africa in the province of Quranwala, India [4], have drawn some speculation to their authenticity. In the Americas, \(\sim\)130 ka anthropic bone breakage [5] are also noted to be located in areas with highly abrasive sediments and problematic taphonomic contexts. The current consensus for the oldest cut marks in Africa, however, remains to be those of Gona [6], dated to approximately between 2.1 and 2.58 Ma, while other promising results have been localized with 1.9 and 2.4 Ma in Northern Africa [7].  
Taphonomic debates revolving around these topics are essential in understanding features of human evolution, considering how current theories argue meat-eating to be a fundamental component of our evolution [8, 9, 10, 11]. The concept of butchery contains a multitude of different implications beginning with resource acquisition [8, 12, 13, 14, 15], as well as the cognitive technical capacities to manufacture the instruments used for such activities [16, 17, 18, 19, 20]. Dates of cut marks at 3.3 Ma implicate Australopithecine populations to be the first users of tools and butchers in hominin pre-history [1], however authors are yet to come to an agreement as to whether these individuals were physically capable of such practices [16, 17, 18, 19, 20]. While an argument has been proposed to say that natural edges of unknapped stones could be used for butchery practices [1], other authors argue that experimentation is yet to be found that supports this claim [3]. Nevertheless, if these findings were to be real, then strong empirical evidence would be needed in support of such a hypothesis.  
Recent advances in the development of new methodologies for the study of Bone Surface Modifications (BSMs) have been able to reveal interesting patterns in the in-depth study of taphonomic traces. The implementation of Geometric Morphometric studies has been able to reveal a means of inferring different tool use [21] as well as raw material management [21, 22, 23] through cut mark morphologies. Moreover, when applied to the carnivore induced BSMs, analysts have been able to differentiate between carnivore agents based on tooth mark morphologies [24, 25, 26, 27]. The innovative introduction of Artificial Intelligence (AI) in taphonomy [26, 27, 28, 29, 30, 31] has additionally been able to overcome multiple barriers imposed by subjectivity [32]. This presents a powerful tool for the construction of classification models, presenting a series of efficient tools for the processing of complex data.  
Here we present the power of Feed Forward Neural Networks (FFNN) trained through Deep Learning (DL) for the processing of morphological data obtained with advanced 3D digital microscopy. These efforts attempt to overcome issues imposed by equifinality and subjectivity in taphonomic research, as well as complement previously obtained data regarding the effectivity of Machine Learning (ML) algorithms for the processing of Geometric Morphometric information [26]. Through this, we present a new means of classifying cut marks and trampling marks through their morphological attributes, as well as an empirically objective and quantitative approximation to their morphological description and characterization.