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

Area of Science:

• Archaeological science
• Materials science
• Lithic analysis

Background:

• Metrology has advanced use-wear analysis on fine-grained stone tools like chert.
• Studies on coarse-grained materials, such as quartzite, remain less frequent.
• Quantifying wear on quartzite presents unique challenges due to its texture.

Purpose of the Study:

• To apply confocal microscopy and machine learning for analyzing wear on quartzite tools.
• To classify wear textures resulting from contact with different worked materials.
• To assess the efficacy of metrological techniques on coarse-grained lithologies.

Main Methods:

• Confocal microscopy was utilized to examine polished quartzite surfaces.
• Machine learning classifiers (decision tree, support-vector machine) were employed.
• Wear parameters (Mean density of furrows, Mean depth of furrows, Core material volume-Vmc) were analyzed.

Main Results:

• The machine learning approach achieved high classification accuracy (100%) for bone and hide.
• Satisfactory classification rates were obtained for other materials, with some overlap noted for cane.
• The study demonstrates the potential of metrology for analyzing wear on quartzite.

Conclusions:

• Confocal microscopy combined with machine learning is effective for use-wear analysis on quartzite.
• This data-driven approach enhances our ability to interpret tool use in archaeology.
• Further research with larger sample sizes is recommended for refining the method.