Graduation Term
2023
Degree Name
Master of Science (MS)
Committee Chair
Shelby S.J. Putt
Abstract
A naturalistic approach in applied forensic research is paramount to reliable expert testimony. Less controlled, or natural, methods in forensic toolmark research are meant to mimic the conditions under which criminal dismemberment would occur; ensuring that the outcomes of such research are applicable in criminal cases. In a monumental study, Steve Symes (1992, Morphology of Saw Marks in Human Bone: Identification of Class Characteristics, University of Tennessee) made experimental cuts into defleshed, degreased human bone with serrated blades and found that blade set width was indicative of kerf width, although kerf width alone could not indicate blade or set type. His work included subjective, qualitative analysis and lacked statistical support. I attempt to quantitatively verify that kerf width and morphology is more diagnostically valuable than previously thought after applying naturalistic alterations to Symes’ (1992) methods by making incomplete cuts into fully fleshed, partially restrained Odocoileus virginianus long bones. Nine western handsaws with rip cut or crosscut blade types, and alternating, wavy or raker set types, made a total of 45 incomplete cuts into deer bones as human proxy. A one-way ANOVA test confirmed a significant difference between at least two sets of kerf widths (P=<0.001). A Tukey HSD test and blind grouping of kerf cross section stills displayed a significant difference between kerfs of different handsaws but was unable to quantify a relationship between kerf width and handsaw characteristics. Blade type was found to indicate kerf morphology, and there is evidence that set type and width contribute to kerf morphology.
Access Type
Thesis-Open Access
Recommended Citation
Patterson, Sarah Elizabeth, "Identifying Handsaws from Cut Marks on Bone: a Microscopic Trait Analysis" (2023). Theses and Dissertations. 1770.
https://ir.library.illinoisstate.edu/etd/1770
DOI
https://doi.org/10.30707/ETD2023.20231004061829930715.999947