Experimental bison butchery using replica hafted Clovis fluted points and large handheld flakes

Eren, Metin I.; Bebber, Michelle R.; Mukusha, Lawrence; Wilson, Michael; Boehm, Andrew R.; Buchanan, Briggs; Miller, G. Logan; Skoglund, Matt; Hayes, John; Barta, Max; Bates, Samantha; Callaghan, Ryan; Floyd, Chester; Morris, Seth; Neuharth, Spencer; Newcomb, Clay; Rinella, Steven; Schneider, Corinne; Smith, Mary Margaret; Parfitt, Anne; Meltzer, David J.

doi:10.1016/j.jasrep.2024.104480

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…This consistency is likely attributable to the used surface's higher re ectance. This phenomenon has also been observed in minimally developed butchering traces, which, despite being described as having a dull polish, are identi able due to their overall higher re ectivity compared to unused surfaces (Eren et al, 2024).…”

Section: Datasetmentioning

confidence: 67%

Archaeological and experimental lithic microwear classification through 2D textural analysis and machine learning

Sferrazza

2024

Preprint

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The paper focuses on introducing 2D texture analysis as a quantitative method for functional analysis in archaeology. Indeed, for the first time, different techniques of quantitative feature extraction and machine learning algorithms applied to the functional analysis of archaeological lithic tools are described and compared. The method presented relies on five techniques of quantitative feature extraction from photographic images and six classification techniques through machine learning algorithms. After creating a training dataset with experimental traces, machine learning models were used to classify 23 images (10 experimental and 13 archaeological). The best result achieved a classification accuracy of 87%, demonstrating the ability to interpret use-wear traces correctly on both experimental and archaeological artefacts regardless of the geological origin of the flint (Sicily in Italy and Sachsen-Anhalt in Germany). The paper proposes to use the method as a fundamental tool in functional analysis to remove subjectivity criteria from traditional analysis and to address issues related to the credibility of the discipline, calibration, standardisation, and reproducibility of methods and results.

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Section: Datasetmentioning

confidence: 67%

Archaeological and experimental lithic microwear classification through 2D textural analysis and machine learning

Sferrazza

2024

Preprint

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Hit or miss: Do microscopic linear impact traces (MLITs) form on Clovis stone tips launched via atlatl into foliage and sediment?

Mukusha,

Logan Miller,

Smith

et al. 2024

Journal of Archaeological Science: Reports

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Controlled comparative tensile tests of backed versus non‐backed edges’ adhesion: Inferences into stone tool functional properties

Wilson,

Buchanan,

Fisch

et al. 2024

Archaeometry

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Backing is a procedure for retouching a stone tool edge to an angle of or near 90°. Archaeologists have recorded backed lithic specimens in the Pleistocene and Holocene around the world. One prominent hypothesis for the occurrence of backing is that it increases a stone tool's adhesion relative to what it would have otherwise been with unmodified, sharp edges. We conducted a highly controlled semi‐static tensile test in which we assessed lithic specimens that possessed both a backed and a non‐backed edge, opposing each other. We hafted each specimen's backed and non‐backed edges to wood, and the bi‐hafted stone implement was then pulled apart using an Universal Instron Materials Tester, allowing for a direct ‘head‐to‐head’ comparison of the two edge types’ adhesive properties. Our tensile test results suggested no significant difference between backed and non‐backed edges in terms of adhesion, which does not support the hypothesis that backing increases a lithic specimen's adhesion.

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Experimental bison butchery using replica hafted Clovis fluted points and large handheld flakes

Cited by 3 publications

References 69 publications

Archaeological and experimental lithic microwear classification through 2D textural analysis and machine learning

Archaeological and experimental lithic microwear classification through 2D textural analysis and machine learning

Hit or miss: Do microscopic linear impact traces (MLITs) form on Clovis stone tips launched via atlatl into foliage and sediment?

Controlled comparative tensile tests of backed versus non‐backed edges’ adhesion: Inferences into stone tool functional properties

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