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Age at death estimation in adult skeletons is hampered, among others, by the unremarkable correlation of bone estimators with chronological age, implementation of inappropriate statistical techniques, observer error, and skeletal incompleteness or destruction. Therefore, it is beneficial to consider alternative methods to assess age at death in adult skeletons. The decrease in bone mineral density with age was explored to generate a method to assess age at death in human remains. A connectionist computational approach, artificial neural networks, was employed to model femur densitometry data gathered in 100 female individuals from the Coimbra Identified Skeletal Collection. Bone mineral density declines consistently with age and the method performs appropriately, with mean absolute differences between known and predicted age ranging from 9.19 to 13.49 years. The proposed method-DXAGE-was implemented online to streamline age estimation. This preliminary study highlights the value of densitometry to assess age at death in human remains.

The use of dental morphology to estimate ancestry has a long history within dental anthropology. Over the past two decades methods employing dental morphology within forensic anthropology have become more formalized with the incorporation of statistical models. We present here on a new application (rASUDAS) to estimate ancestry of unknown individuals using crown and root morphology of the dentition. The reference sample is composed of 21 traits based on the Arizona State University Dental Anthropology System and represents approximately 30,000 individuals from seven geographic regions. The statistical program was created in R and uses a naïve Bayes classifier algorithm to assign posterior probabilities for individual group assignment. A random sample of 150 individuals from the dataset was chosen and input into the program. In a sevengroup analysis, the model was correct in group assignment 51.8% of the time. In a four-group analysis, classification improved to 66.7%, and with only three groups considered the accuracy improved to 72.7%. It is still necessary to validate the program using forensic cases and to augment the reference sample with modern skeletal data. However, we present these results as a proof of concept of the statistical application and the use of dental morphology in the estimation of ancestry.

The assessment of sex is crucial to the establishment of a biological profile of an unidentified skeletal individual. The best methods currently available for the sexual diagnosis of human skeletal remains generally rely on the presence of well-preserved pelvic bones, which is not always the case. Postcranial elements, including the femur, have been used to accurately estimate sex in skeletal remains from forensic and bioarcheological settings. In this study, we present an approach to estimate sex using two measurements (femoral neck width [FNW] and femoral neck axis length [FNAL]) of the proximal femur. FNW and FNAL were obtained in a training sample (114 females and 138 males) from the Luís Lopes Collection (National History Museum of Lisbon). Logistic regression was used to develop a model to predict sex in unknown individuals. The logistic regression model correctly predicted sex in 85.3% to 85.7% of the cases. The model was also evaluated in a test sample (96 females and 96 males) from the Coimbra Identified Skeletal Collection (University of Coimbra), resulting in a sex allocation accuracy of 80.1% to 86.2%. This study supports the relative value of the proximal femur to estimate sex in skeletal remains, especially when other exceedingly dimorphic skeletal elements are not accessible for analysis.

ObjectivesComplete and accurate human skeletal inventory is seldom possible in archaeological and forensic cases involving severe fragmentation. In such cases, skeletal mass comparisons with published references may be used as an alternative to assess skeletal completeness but they are too general for a case-by-case routine analysis. The objective is to solve this issue by creating linear regression equations to estimate the total mass of a skeleton based on the mass of individual bones. Material and MethodsTotal adult skeletal mass and individual mass of the clavicle, humerus, femur, patella, carpal, metacarpal, tarsal and metatarsal bones were recorded in a sample of 60 skeletons from the 21 st century identified skeletal collection (University of Coimbra). The sample included 32 females and 28 males with ages ranging from 31 to 96 years old (mean = 76.4; sd = 14.8). Skeletal mass linear regression equations were calculated based on this sample. ResultsThe mass of individual bones was successfully used to predict the approximate total mass of the adult skeleton. The femur, humerus, and second metacarpal were the best predictors of total skeletal mass with root mean squared errors ranging from 292.9 to 346.1 gm. DiscussionLinear regression was relatively successful at estimating adult skeletal mass. The non-normal distribution of the sample in terms of mass may have reduced the predictive power of the equations. These results have clear impact for bioanthropology, especially forensic anthropology, since this method may provide better estimates of the completeness of the skeleton or the minimum number of individuals.Keywords: bioarchaeology; forensic anthropology; bone mass; scattered remains; funerary practice.The objective of this paper is to investigate the potential of linear regression to estimate the mass of human adult skeletons based on the mass of individual bones. In some cases involving skeletal remains, it may be difficult to assess how complete the skeleton is due to fragmentation that prevents the anatomical identification of all skeletal elements. For example, it may be difficult to estimate the minimum number of individuals (MNI) or decide when to conclude forensic searches for the remains of victims when it is impossible to determine the amount of missing bones, especially if the remains are very fragmented, commingled and/or scattered. Such inventory problems are often more complicated still in cases involving burned skeletal remains. In the case of archaeological cremations, an exhaustive inventory is often impossible to accomplish due to the high number of anatomically unidentified fragments (Gonçalves et al, 2015). Therefore, unorthodox methods to assess skeletal completeness are worth exploration.To our knowledge, the only alternative method to assess skeleton completeness is by weighing remains to provide an estimate of skeletal mass. This is then compared with references obtained from samples of complete adult skeletons (e.g. Ingalls, 1931;Lowrance and Latimer, 1957;Silva et al.,...

Background Paleoanthropological research focus still devotes most resources to areas generally known to be fossil rich instead of a strategy that first maps and identifies possible fossil sites in a given region. This leads to the paradoxical task of planning paleontological campaigns without knowing the true extent and likely potential of each fossil site and, hence, how to optimize the investment of time and resources. Yet to answer key questions in hominin evolution, paleoanthropologists must engage in fieldwork that targets substantial temporal and geographical gaps in the fossil record. How can the risk of potentially unsuccessful surveys be minimized, while maximizing the potential for successful surveys? Methods Here we present a simple and effective solution for finding fossil sites based on clustering by unsupervised learning of satellite images with the k-means algorithm and pioneer its testing in the Urema Rift, the southern termination of the East African Rift System (EARS). We focus on a relatively unknown time period critical for understanding African apes and early hominin evolution, the early part of the late Miocene, in an overlooked area of southeastern Africa, in Gorongosa National Park, Mozambique. This clustering approach highlighted priority targets for prospecting that represented only 4.49% of the total area analysed. Results Applying this method, four new fossil sites were discovered in the area, and results show an 85% accuracy in a binary classification. This indicates the high potential of a remote sensing tool for exploratory paleontological surveys by enhancing the discovery of productive fossiliferous deposits. The relative importance of spectral bands for clustering was also determined using the random forest algorithm, and near-infrared was the most important variable for fossil site detection, followed by other infrared variables. Bands in the visible spectrum performed the worst and are not likely indicators of fossil sites. Discussion We show that unsupervised learning is a useful tool for locating new fossil sites in relatively unexplored regions. Additionally, it can be used to target specific gaps in the fossil record and to increase the sample of fossil sites. In Gorongosa, the discovery of the first estuarine coastal forests of the EARS fills an important paleobiogeographic gap of Africa. These new sites will be key for testing hypotheses of primate evolution in such environmental settings.

The pelvis is consistently regarded as the most sexually dimorphic region of the human skeleton, and methods for sex estimation with the pelvic bones are usually very accurate. In this investigation, population-specific osteometric models for the assessment of sex with the pelvis were designed using a dataset provided by J.A. Serra (1938) that included 256 individuals (131 females and 125 males) from the Coimbra Identified Skeletal Collection and 38 metric variables. The models for sex estimation were operationalized through an online application and decision support system, CADOES. Different classification algorithms generated high accuracy models, ranging from 85% to 92%, with only three variables; and from 85.33% to 97.33%, with all 38 variables. CADOES conveys a probabilistic prediction of skeletal sex, as well as a suite of attributes with educational applicability in the fields of human skeletal anatomy and statistics. This study upholds the value of the pelvis for the estimation of skeletal sex and provides models for that can be applied with high accuracy and low bias.

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