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.

Many archeologists are skeptical about the capabilities of use-wear analysis to infer on the function of archeological tools, mainly because the method is seen as subjective, not standardized and not reproducible. Quantitative methods in particular have been developed and applied to address these issues. However, the importance of equipment, acquisition and analysis settings remains underestimated. One of those settings, the numerical aperture of the objective, has the potential to be one of the major factors leading to reproducibility issues. Here, experimental flint and quartzite tools were imaged using laser-scanning confocal microscopy with two objectives having the same magnification but different numerical apertures. The results demonstrate that 3D surface texture ISO 25178 parameters differ significantly when the same surface is measured with objectives having different numerical apertures. It is, however, unknown whether this property would blur or mask information related to use of the tools. Other acquisition and analyses settings are also discussed. We argue that to move use-wear analysis toward standardization, repeatability and reproducibility, the first step is to report all acquisition and analysis settings. This will allow the reproduction of use-wear studies, as well as tracing the differences between studies to given settings.

The scale-sensitive fractal analysis (SSFA) of dental microwear textures is traditionally performed using the software Toothfrax. SSFA has been recently integrated to the software MountainsMap® as an optional module. Meanwhile, Toothfrax support has ended. Before switching to the new module, the outputs between the two software packages must be compared for consistency. We have performed such a test using Bayesian modelling on three datasets including dental surfaces of sheep (Merceron, Ramdarshan, et al., 2016) and guinea pigs (Winkler, Schulz-Kornas, Kaiser, Cuyper, et al., 2019) from controlled feeding experiments, as well as surfaces of quartzite and flint flakes used in an actualistic archeological experiment on cleaning procedures (Pedergnana, Calandra, Bob, et al., 2020). We found that the two software packages calculate significantly different values for the SSFA parameters epLsar, Asfc, HAsfc9 and R 2 , even when the same settings are used. Nevertheless, the treatments (different diets or cleaning procedures) are discriminated similarly within each dataset. While the new software module is as good as the original software to differentiate treatments, our results imply that the outputs from the two software packages are not directly comparable and, as such, cannot be merged. Surface texture analysts should therefore consider re-analyzing published surfaces before integrating them in their studies.

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.

Timely information on current infection numbers during an epidemic is of crucial importance for decision makers in politics, medicine, and businesses. As information about local infection risk can guide public policy as well as individual behavior, such as the wearing of personal protective equipment or voluntary social distancing, statistical models providing such insights should be transparent and reproducible as well as accurate. Fulfilling these requirements is drastically complicated by the large amounts of data generated during exponential growth of infection numbers, and by the complexity of common inference pipelines. Here, we present CorCast -- a stable and scalable distributed architecture for the reproducible estimation of nowcasts suitable for pandemic scenarios -- and its application to the inference of district-level SARS-CoV-2 infection numbers in Germany.