ArticlePloS one2020
Polish is quantitatively different on quartzite flakes used on different worked materials.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Classifying polish in use-wear analysis with convolutional neural networks.Scientific reports · 2025Article
- Multiscale Geometric Characterization and Discrimination of Dermatoglyphs (Fingerprints) on Hardened Clay-A Novel Archaeological Application of the GelSight Max.Materials (Basel, Switzerland) · 2025Article
- Beyond the Surface: Exploring Ancient Plant Food Processing through Confocal Microscopy and 3D Texture Analysis on Ground Stone Tools.Journal of archaeological method and theory · 2025Article
- The effect of worked material hardness on stone tool wear.PloS one · 2022Article
- Optimization of use-wear detection and characterization on stone tool surfaces.Scientific reports · 2021Article
- Quantitative use-wear analysis of stone tools: Measuring how the intensity of use affects the identification of the worked material.PloS one · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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