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

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.

Quantitative use-wear analysis of stone tools: Measuring how the intensity of use affects the identification of the worked material

“…Already over 30 years ago, several scholars, using different methodologies, demonstrated that polish generated by diverse worked materials could be quantitatively discriminated [ 21 – 24 , 28 , 41 – 44 ]. During the last decade, confocal microscopy and texture analysis have been used for the same aim, showing the great potential of this methodology [ 30 , 31 , 5 – 47 ] for tools made with different rocks, including non-homogeneous ones, like quartzite [ 48 ]. Confocal microscopy has taken this approach one step forward, as it is a precise and easy-to-use method for the analysis of textures.…”

“…Use-wear from different worked materials (plant, animal) [ 37 , 39 , 41 ] and on different raw -materials (flint, pottery, bones, etc.) [ 35 , 48 , 49 , 51 , 52 ] are tested and gradually included in this field of study. All these pilot studies will build a consensus on practices to be followed.…”

“…Regarding the first problem, we observed that the variability among different types of fine-grained flint does not affect texture analysis substantially [ 36 ] except for the identification of meat-cutting activities. However, the analysis of coarse-grained flints and other rocks will require the development of specific experimental programs [ 48 ]. As regards post-depositional alterations, our previous analysis of archaeological tools was carried out on well preserved Neolithic glossed tools [ 37 – 39 ].…”

The effect of worked material hardness on stone tool wear

“…The problem is manifold: not all polishes are equally easy to see and interpret, and not all types of data deliver the same answers. Some types of materials, such as cereals [9,10] or antler, bone, and ivory (ABI) have been identified consistently in blind tests or have been separated quantitatively in experimental settings with varying degrees of success (see [11][12][13][14][15]). Traces left by others, especially by soft and elastic materials, such as meat, tendons, etc., are less identifiable [16].…”

Optimization of use-wear detection and characterization on stone tool surfaces

“…By maximizing the differences, masks might also improve the number of correctly classified types of wear by machine learning algorithms. Recent studies provided mixed results of classification of contact material 29 , 32 , 33 . In these studies, good classifications are often under 70%.…”