Year of Award

2020

Document Type

Dissertation

Degree Type

Doctor of Philosophy (PhD)

Degree Name

Anthropology

Department or School/College

Department of Anthropology

Committee Chair

Meradeth Snow

Commitee Members

Brandon Bridge, Kirsten Green, Aldo Fusaro, Ashley Kendell

Keywords

Forensic Anthropology, Geometric Morphometric, Morphology, Pelvis, Sex, Sex Determination

Publisher

University of Montana

Abstract

Geometric morphometrics has become a more popular method in anthropology as three-dimensional data and research become more widely recognized and accessible. This research provides a refined method utilizing 3-D geometric morphometric analysis to determine sex from the human pubic bone. The study used a sample of N=378 individual pubic bones from the University of New Mexico Maxwell Documented Collection. Eight landmarks were digitized on each individual bone using a Microscribe Digitizer. Results from the Principal Components Analysis provide promising clustering between male and female groups, as well as indications that the method may be ancestry-specific, and that parity may have an effect on the shape of female pubic bones. The Discriminant Function analysis of the training data set resulted in 96.2% accuracy in predicting the correct sex, and the testing data set resulted in 95.5% accuracy, P<0.0001. To test the ability to replicate this method, the author collected data a second time on a random set of 50 individuals, N=100 pubic bones and reran the GPA, PCA, and discriminant function analyses. This second test resulted in 96.5% accuracy of the training data set, and 93.8% accuracy of the testing data set. To test interobserver error, the author collected all eight landmarks from the same bone once a day, six days in a row. The PCA scatter plot of this test is presented to exhibit the extremely low variance between each instance of measurement. In order to make this method more applicable to real casework, a modeled fragmentary analysis was also conducted. Statistical analyses and machine learning algorithms were used to mimic fragmented remains that included tests run on each possible landmark combination of three or more landmarks to simulate fragmented bones (218 combinations). The results of the modeled fragmentary analysis consisted of 133 combinations which exhibit a 90% or higher accuracy in sex prediction; and nine combinations which exhibit 95.5% accuracy in sex prediction. In particular, three landmarks clustered around the ventral arc of the pubic bone performed the best, indicating this is the most sexually dimorphic portion of the bone. These results indicate that three-dimensional geometric morphometrics is a valid method to be applied to sex determination in forensic anthropology.

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© Copyright 2020 Katherine Scot Baca