Bayesian sex estimation from permanent canine metrics in an Argentinian documented collection

Abstract

Objective: To validate a Bayesian method for sex estimation from permanent canines (Luna, 2019) on an independentcontemporary skeletal sample from Argentina.Design: The study analyzed 152 permanent canines (61 upper, 91 lower) from 98 individuals (55 males, 43 females)included in the Prof. Dr. R´omulo Lambre Reference Collection (La Plata, Argentina). Buccolingual andmesiodistal diameters of the crown and neck region were measured. Bayesian posterior probabilities werecomputed for single- and multi-variable models, and classification accuracy was assessed against documentedbiological sex.Results: The buccolingual neck diameter was the best single predictor, yielding accuracy rates of 73.77% (lowercanines, p = 0.74) and 73.62% (lower canines, p = 0.74). Multi-variable models improved performance: usingonly the neck diameter, lower canines achieved 81.96% accuracy (p = 0.78), and upper canines, 80.21%(p = 0.80). The four-variable model produced the highest overall accuracy (lower: 85.24%, p = 0.85; upper:83.51%, p = 0.83). Sex-specific correct classification rates ranged 80.77–83.33% for females and 83.63–88.57%for males, with posterior probabilities of 0.78–0.88 and 0.80–0.86, respectively.Conclusions: Neck metrics demonstrate strong performance for sex estimation, proving particularly valuable forcommingled or isolated remains. This Bayesian probabilistic framework provides transparent uncertainty estimatesessential for forensic standards and can be successfully applied to contemporary non-identified individualsof similar geographical provenance.