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Metrology has been successfully used in the last decade to quantify use-wear on stone tools. Such techniques have been mostly applied to fine-grained rocks (chert), while studies on coarse-grained raw materials have been relatively infrequent. In this study, confocal microscopy was employed to investigate polished surfaces on a coarse-grained lithology, quartzite. Wear originating from contact with five different worked materials were classified in a data-driven approach using machine learning. Two different classifiers, a decision tree and a support-vector machine, were used to assign the different textures to a worked material based on a selected number of parameters (Mean density of furrows, Mean depth of furrows, Core material volume-Vmc). The method proved successful, presenting high scores for bone and hide (100%). The obtained classification rates are satisfactory for the other worked materials, with the only exception of cane, which shows overlaps with other materials. Although the results presented here are preliminary, they can be used to develop future studies on quartzite including enlarged sample sizes.

Shamanic belief systems represent the first form of religious practice visible within the global archaeological record. Here we report on the earliest known evidence of shamanic costume: modified red deer crania headdresses from the Early Holocene site of Star Carr (c. 11 kya). More than 90% of the examples from prehistoric Europe come from this one site, establishing it as a place of outstanding shamanistic/cosmological significance. Our work, involving a programme of experimental replication, analysis of macroscopic traces, organic residue analysis and 3D image acquisition, metrology and visualisation, represents the first attempt to understand the manufacturing processes used to create these artefacts. The results produced were unexpected--rather than being carefully crafted objects, elements of their production can only be described as expedient.