Revisiting metric sex estimation of burnt human remains via supervised learning using a reference collection of modern identified cremated individuals (Knoxville, USA).

Our study demonstrated the potential of machine learning approaches, such as neural networks, for multivariate analyses. Using these statistical methods improves the rate of correct sex estimations in calcined human remains and can be applied to highly fragmented unburnt individuals from both archaeological and forensic contexts.