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Forensic anthropology

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From Prairie To Practice: Forensic Taphonomy, Ethics, And Regional Ecology In Southeastern Nebraska, Jessica F. L. Gammage May 2026

From Prairie To Practice: Forensic Taphonomy, Ethics, And Regional Ecology In Southeastern Nebraska, Jessica F. L. Gammage

Department of Anthropology: Theses and Student Research

Forensic taphonomy examines the postmortem processes that affect human remains within specific environmental contexts and plays a critical role in medicolegal death investigation. Despite significant advances in decomposition research, experimental studies remain geographically and environmentally uneven, limiting the transferability of postmortem interval (PMI) estimation methods across regions. The Central Great Plains are underrepresented in forensic taphonomic research despite its distinct ecological and geomorphological characteristics.

This thesis evaluates southeastern Nebraska as a specific regional setting for forensic taphonomic research by integrating ecological, pedological, and geomorphological data with current forensic literature. Tallgrass prairie vegetation, loess-derived soils, seasonal moisture variability, and dynamic fluvial …


Non-Destructive Automated Classification Of Human And Large Mammal Long Bone Fragments: A Deep Learning Approach Using Micro-Ct Histomorphology And Grad-Cam Interpretation, Kathleen Marie Kelley May 2026

Non-Destructive Automated Classification Of Human And Large Mammal Long Bone Fragments: A Deep Learning Approach Using Micro-Ct Histomorphology And Grad-Cam Interpretation, Kathleen Marie Kelley

Department of Anthropology: Theses and Student Research

This study explores the development and validation of an automated, deep learning system designed to differentiate human from large mammal long bone fragments using micro-CT imagery. A comprehensive dataset of cross-sectional µCT images was assembled from human and animal skeletal material processed at the DPAA, Nebraska. A convolutional neural network (CNN) based on a ResNet-18 architecture, utilizing transfer learning from ImageNet weights, was trained to classify µCT images as human or animal. The model achieved a mean classification accuracy of 89.85% (± 8.27%) across five sample-level cross-validation folds, with a sensitivity of 96.21% and a 95% bootstrap confidence interval of …