Polish is quantitatively different on quartzite flakes used on different worked materials

PLOS ONE RESEARCH ARTICLE Polish is quantitatively different on quartzite flakes used on different worked materials Antonella Pedergnana ID1☯*, Ivan Calandra1☯, Adrian A. Evans2‡, Konstantin Bob ID3☯, Andreas Hildebrandt3‡, Andreu Ollé4,5‡ a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 TraCEr, Laboratory for Traceology and Controlled Experiments at MONREPOS Archaeological Research Centre and Museum for Human Behavioural Evolution, RGZM, Neuwied, Germany, 2 School of Life Sciences, University of Bradford, Bradford, West Yorkshire, United Kingdom, 3 Scientifc Computing and Bioinformatics, Institute of Computer Science, Johannes Gutenberg University, Mainz, Germany, 4 IPHES, Institut Català de Paleoecologia Humana i Evolució Social, Tarragona, Spain, 5 Departament d’Història i Història de l’Art, Universitat Rovira i Virgili, Tarragona, Spain ☯ These authors contributed equally to this work. ‡ These authors also contributed equally to this work. * , Abstract OPEN ACCESS Citation: Pedergnana A, Calandra I, Evans AA, Bob K, Hildebrandt A, Ollé A (2020) Polish is quantitatively different on quartzite flakes used on different worked materials. PLoS ONE 15(12): e0243295. https://doi.org/10.1371/journal. pone.0243295 Editor: Marco Peresani, Universita degli Studi di Ferrara, ITALY Received: August 26, 2020 Accepted: November 19, 2020 Published: December 3, 2020 Copyright: © 2020 Pedergnana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All data generated and/or analyzed during the current study are included in this published article and its Supporting information files, or are available on Zenodo (https://doi.org/10.5281/zenodo.3979116 for the ConfoMap analysis, https://doi.org/10.5281/ zenodo.3979139 for the R analysis, and https://doi. org/10.5281/zenodo.3979161 for the Python analysis). Funding: This research has been supported within the Römisch-Germanisches Zentralmuseum – 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. Introduction Quantification of use-wear has recently seen an increasing interest among specialists [1, 2 and references therein]. Use-wear studies using metrology can provide a robust and quantitative approach to analysis, and they have the potential to improve and complement previous qualitative methodologies, which have performed poorly in blind-tests [3–6]. Several techniques have been used to acquire 3D data in order to quantify use-wear, such as focus variation microscopy, laser profilometry, white-light interferometry and laser scanning confocal microscopy (LSCM) [1, 7–15]. Chert i.e. fine-grained silica sedimentary rocks, sensu [16], has been the most studied raw material in conventional use-wear studies which included large experimental datasets [e.g. 17–19]. Similarly, quantitative methods have mainly been applied on PLOS ONE | https://doi.org/10.1371/journal.pone.0243295 December 3, 2020 1 / 27 PLOS ONE Leibniz Research Institute for Archaeology by German Federal and Rhineland Palatinate funding (Sondertatbestand “Spurenlabor”), the Spanish MICINU-FEDER project PGC2018-093925-B-C32, the Catalan AGAUR project 2017-SGR-1040, the URV project 2019-PFR-URV-91 and the Fragmented Heritage Project (AH/L00688X/1). Competing interests: The authors have declared that no competing interests exist. Polish is quantitatively different on quartzite flakes used on different worked materials chert surfaces [7, 10, 12, 20, 21], with few attempts done to assess their potential for the analysis of coarse-grained rocks [22–24]. Trials on other raw materials, such as obsidian or basalt, have also been performed in the past [14, 25–27]. Quantitative surface analysis can be applied to materials other than rocks [2]. In fact, surfaces of ochre, bone and shells have also been analyzed mainly using confocal microscopy [28–31]. Nevertheless, quantification studies are still in their infancy and none of the tested techniques have systematically been incorporated into the domain of traceology [6]. Among the various techniques used to acquire 3D surface topographical data, data acquired with confocal microscopy proved to be able to discern contact materials obtained from experimentally produced polished surfaces on chert specimens [10, 12, 32]. LSCM was preferred over the other available techniques due to its ease of use, relatively quick acquisition time and inherent potential demonstrated by the initial studies that incorporated relatively small datasets [20, 22, 33, but see 34]. Confocal microscopes are generally coupled with optical microscopes, which are useful for observing areas to be measured [12, 35, 36]. 3D topographies are generally acquired to provide quantitative data of the worn areas resulting from contact with different materials. The main underlying goal of doing this is to limit the analysts’ subjectivity and to increase the general accuracy of the method [6, 37]. Moreover, it improves repeatability and reproducibility of the analyses [38]. However, it has been shown that it is not yet possible to automatically locate and isolate the worn areas (i.e. areas of interest) for analysis [20], implying that the choice of the area to be analyzed is still subject to the analyst’s discretion. In this regard it complements the ‘traditional’ microwear method in that the one aspect that has performed well in prior blind testing is the expert analyst’s ability to identify the location of wear [6]. A further reason that explains the high investment of energy and time into developing and refining quantitative methods in use-wear analysis is the possibility of producing probability statements based on surface parameters and the use of a variety of statistical (...truncated)