Results of a large-scale humeri pair matching study.

During the analysis of a commingled human remains assemblage, pair-matching is often conducted as part of the inventory to inform the DNA sampling strategy and to calculate the minimum or most likely number of individuals. As commingled assemblages become larger, pair-matching becomes more difficult, and it is unknown whether accuracy declines. Therefore, a study to determine the accuracy rates of visual pair-matching for multiple observers was conducted on a large, commingled human assemblage.

Forensic anthropologists and estimates of skeletal completeness: The impacts of training and experience.

Forensic anthropologists engage with numerous and diverse stakeholders in their casework. Regarding the recovery of human remains, these stakeholders may be interested in quantifying or qualifying the amount of remains recovered. How forensic anthropologists respond to such questions, whether verbally or in written reporting, has the potential to impact the trajectory of a case.

Assessing recovery rate methods: A review and application for human skeletal assemblages.

Objectives: Recovery rates reflect the amount of recovered skeletal materials based on expectations about the total number of elements or individuals that should be present in an assemblage. It is an underlying concept that reflects analytical potential, wherein high recovery rates typically indicate high analytical capabilities. However, numerous methods are available to calculate different types of recovery rates, and each method addresses various types of research questions and utilizes different variables.

Evaluation of method performance for osteometric sorting of commingled human remains.

Evaluation of method performance involves the consideration of numerous factors that can contribute to error. A variety of measures of performance can be borrowed from the signal detection literature and others are drawn from statistical science. This article demonstrates the principles of performance evaluation by applying multiple measures to osteometric sorting models for paired elements run against data from known individuals.