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

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 inves...

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Main Authors: Antonella Pedergnana, Ivan Calandra, Adrian A Evans, Konstantin Bob, Andreas Hildebrandt, Andreu Ollé
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0243295
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spelling doaj-1bf66b3c819040f18f4dbaefefc915532021-03-04T12:49:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011512e024329510.1371/journal.pone.0243295Polish is quantitatively different on quartzite flakes used on different worked materials.Antonella PedergnanaIvan CalandraAdrian A EvansKonstantin BobAndreas HildebrandtAndreu OlléMetrology 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.https://doi.org/10.1371/journal.pone.0243295
collection DOAJ
language English
format Article
sources DOAJ
author Antonella Pedergnana
Ivan Calandra
Adrian A Evans
Konstantin Bob
Andreas Hildebrandt
Andreu Ollé
spellingShingle Antonella Pedergnana
Ivan Calandra
Adrian A Evans
Konstantin Bob
Andreas Hildebrandt
Andreu Ollé
Polish is quantitatively different on quartzite flakes used on different worked materials.
PLoS ONE
author_facet Antonella Pedergnana
Ivan Calandra
Adrian A Evans
Konstantin Bob
Andreas Hildebrandt
Andreu Ollé
author_sort Antonella Pedergnana
title Polish is quantitatively different on quartzite flakes used on different worked materials.
title_short Polish is quantitatively different on quartzite flakes used on different worked materials.
title_full Polish is quantitatively different on quartzite flakes used on different worked materials.
title_fullStr Polish is quantitatively different on quartzite flakes used on different worked materials.
title_full_unstemmed Polish is quantitatively different on quartzite flakes used on different worked materials.
title_sort polish is quantitatively different on quartzite flakes used on different worked materials.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description 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.
url https://doi.org/10.1371/journal.pone.0243295
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