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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Online Access: | https://doi.org/10.1371/journal.pone.0243295 |
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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 |
work_keys_str_mv |
AT antonellapedergnana polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials AT ivancalandra polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials AT adrianaevans polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials AT konstantinbob polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials AT andreashildebrandt polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials AT andreuolle polishisquantitativelydifferentonquartziteflakesusedondifferentworkedmaterials |
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