Microwear analysis on experimental ground stone earth-working implements and its implication for investigating ancient agricultural practices

Xie, Lin; Gallo, Tiziana; Macdonald, Danielle A.

doi:10.1016/j.jasrep.2019.04.017

Cited by 1 publication

(2 citation statements)

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“…For example, it has not been systematically employed in the study of coarse-grained rocks, such as quartzite [ 22 , 23 ]. The potential to image use-wear on this type of rock has recently been shown [ 35 , 36 , 41 ]. The systematic documentation of the areas to be measured with the optical microscope mounted on the same confocal microscope used to acquire 3D surfaces allows analysts to carefully select the areas of interest.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Pedergnana¹,

Calandra²,

Evans

et al. 2020

PLoS ONE

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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.

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