Evaluating the microscopic effect of brushing stone tools as a cleaning procedure

Abstract

Cleaning stone tool surfaces is a common procedure in lithic studies. The first step widely applied at any archaeological site (and/or at field laboratories) is the gross removal of sediment from the surfaces of artifacts. Lithic surface alterations due to mechanical action applied in wet or dry cleaning regimes have never been examined at a microscopic scale. This could have important implications in traceology, as any modern surface modifications inflicted on archaeological artifacts might compromise their functional interpretations. The current trend toward quantification of use-wear traces makes the testing even more important, as even slight, apparently invisible surface alterations might be measured.In order to evaluate the impact of common cleaning procedures, we undertook a controlled experiment. The main aim of this experiment was to assess the effects that brushing actions applied for removing sediment particles have on flint and quartzite surfaces.All surfaces were analyzed with confocal microscopy before and after having been brushed to quantify possible changes in the micro-topography. Surface roughness parameters (ISO 25178-2 among others) were applied.Nine parameters changed significantly when mechanical actions were applied to lithic surfaces, meaning that some changes in the surface micro-topography were detected. Therefore, archeologists need to be cautious when applying prolonged mechanical actions for cleaning archaeological stone tools.

The effect of eraser sampling for proteomic analysis on Palaeolithic bone surface microtopography

“…A previous study on experimentally worked bone surfaces found a similar pattern associated with duration of use when bones were worn against fresh animal skin, a supple, sticky material that readily incorporates external particles adding to its abrasiveness 48 . Another experimental study testing the effects of cleaning procedures on stone tools also found an increase in this surface texture parameter after rubbing dirt off of a flint flake for about 1 min 65 . As with fresh skin and sediment particles, this increase in Smrk1 indicates that eraser use causes alterations to the highest portion of bone surfaces resulting in the plateauing of the microtopography at the µm-scale.…”

Use and Misuse of Machine Learning in Anthropology

“…To date, ML has been applied in a number of lithic studies addressing a wide variety of anthropological questions: identifying heat-treated raw material nodules, a practice employed to improve the ease of working raw nodules into stone artifacts [15]; identifying the materials worked by a stone tool according to the classification of the use-wear created on its edge [16], [17]; predicting the original flake mass from variables on the striking platform in order to quantify the degree of resharpening (and thus the length of its use-life as a tool) [18]; predicting site formation conditions from the surface alteration of the site's lithic artifacts [19]; creating more quantitatively rigorous approaches to the creation of typologies for studying artifact shape through time and space [20], [21]; predicting the raw material of the stone tool from the cut marks produced by the edge [22]; identifying the geochemical signatures of geological sources of lithic raw materials as a means of studying prehistoric mobility and material selection criteria [23], [24]; distinguishing the flake products from different reduction strategies for exploiting the volume of a core [25]; distinguishing chronological manifestations of lithic behavior between the Middle and Late Stone Age in Africa through the presence vs. absence of types within assemblages [26]; developing virtual knapping software [27]; and quantifying lithic knapping skill acquisition for studying the evolution of human cognition [28].…”

“…Due to the difficulty interpreting the t-SNE embedding, the "outliers" removed could in fact be valid datapoints that are simply difficult to classify, thereby artificially increasing accuracy scores. In [16] missing data was filled in with the median of the features from its class.…”