Peer Review #3 of "Application of artificially intelligent systems for the identification of discrete fossiliferous levels (v0.1)"

Abstract

The separation of discrete fossiliferous levels within an archaeological or palaeontological site with no clear stratigraphic horizons has historically been carried out using qualitative approaches, relying on two-dimensional transversal and longitudinal projection planes.Analyses of this type, however, can often be conditioned by subjectivity based on the perspective of the analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition techniques in the automated separation and identification of fossiliferous levels. This approach can be divided into three main steps including; (1) unsupervised Machine Learning for density based clustering, (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level models. For evaluation of these techniques, this method was tested in two Late Miocene sites of the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, whereas another three have been differentiated in Batallones-10.Manuscript to be reviewed 46 47 48 49 50 Abstract 51 52The separation of discrete fossiliferous levels within an archaeological or palaeontological site 53 with no clear stratigraphic horizons has historically been carried out using qualitative 54 approaches, relying on two-dimensional transversal and longitudinal projection planes. Analyses 55 of this type, however, can often be conditioned by subjectivity based on the perspective of the 56 analyst. This study presents a novel use of Machine Learning algorithms for pattern recognition 57 techniques in the automated separation and identification of fossiliferous levels. This approach 58 can be divided into three main steps including; (1) unsupervised Machine Learning for density 59 based clustering, (2) expert-in-the-loop Collaborative Intelligence Learning for the integration of 60 geological data followed by (3) supervised learning for the final fine-tuning of fossiliferous level 61 models. For evaluation of these techniques, this method was tested in two Late Miocene sites of 62 the Batallones Butte paleontological complex (Madrid, Spain). Here we show Machine Learning 63 analyses to be a valuable tool for the processing of spatial data in an efficient and quantitative 64 manner, successfully identifying the presence of discrete fossiliferous levels in both Batallones-3 65 and Batallones-10. Three discrete fossiliferous levels have been identified in Batallones-3, 66 whereas another three have been differentiated in Batallones-10.