A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

Distinguishing between tridactyl (three-toed) dinosaur tracks of the herbivorous ornithischians and the predominantly carnivorous theropods is a complex and long-standing problem [1–9]. Broadly, ornithischian tracks are expected to be wider and more symmetric than theropod tracks, with digit impression III less projecting beyond digit impressions II and IV, and with digit impressions being broader, more splayed apart, and terminating in blunt hoof marks instead of sharp claw marks. However, any of these characteristics can be found in both groups, and which are the most important depends on the particular track type in question. Moratalla et al. [1] presented a quantitative approach to discriminate these groups, albeit limited to larger theropod and ornithopod tracks. Limitations of this approach include the small sample size, issues with the measurement scheme and omission of relevant shape characteristics [1,5,6]; despite this, the method has found wide application [3,9–12]. To overcome the limitations of previous statistical approaches, and to remove as much subjectivity as possible, we trained and then employed an artificial neural network to categorize outlines of tridactyl dinosaur footprints as theropod or ornithischian. Artificial neural networks are a type of nonlinear model that can learn from data, and a principal component of machine learning and artificial intelligence. Inspired by the structure of the human brain, such neural networks comprise interconnected nodes (or neurons), with each connection represented by a number (weight).