It is the little things that count: microartifact analysis and the importance of multiproxy data at the widows creek site, alabama

Cyr, Howard; Rimer, Esther; Carmody, Stephen B.; Hollenbach, Kandace D.; Little, Keith J.; Johnson, Hunter B.

doi:10.1179/2168472315y.0000000011

Cited by 5 publications

(2 citation statements)

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“…The spatial analysis of microdebitage can elucidate where and how stone tools were being made at archaeological sites (Cyr et al, 2016; Homsey-Messer and Humkey, 2016; Johnson et al, 2016; Ortmann and Schmidt, 2016; Parker and Sharratt, 2017; Sherwood et al, 1995). Without analyzing the microdebitage, these activity areas cannot be located, and important information regarding the utilization of space, intra-household activities, and intra-site relationships is lost.…”

Section: Methods: Present Studymentioning

confidence: 99%

“…These small artifacts, known as microdebitage, often become embedded into the soils where they were first deposited through human processes, such as trampling and sweeping. Unlike larger facts, including the tools themselves, that are easily cleaned and/or removed from the areas where they were first made, microdebitage are much more resistant to post-depositional movement caused by natural and human processes, such as erosion, bioturbation, and cleaning (Cyr et al, 2016; Homsey-Messer and Humkey, 2016; Johnson et al, 2016; Ortmann and Schmidt, 2016; Parker and Sharratt, 2017; Sherwood et al, 1995). Because of this, the analysis of microdebitage can provide archaeologists with important information regarding where stone tools were being made in prehistory.…”

Section: Introductionmentioning

confidence: 99%

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Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito

Johnson

Eberl

Aguila

et al. 2022

North American Archaeologist

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The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take multiple hours spread over several days to complete. Because of this, microdebitage analysis is typically completed in very small areas of sites due to the intensive time and labor commitment required. Recently, however, my colleagues and I have developed a novel, interdisciplinary method that combines dynamic image analysis and machine learning to analyze microdebitage taken from soil samples at archaeological sites. Analyses of experimental microdebitage demonstrated that microdebitage could be accurately and efficiently identified within archaeological soil samples using this method. In the present study, we apply these methods to soil samples taken from the Maya Capital of Tamarindito in Guatemala to verify whether these methods remain accurate when applied to archaeological contexts.

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