Natalie Ray

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eLife Assessment This manuscript presents an important machine-learning-based approach to the automated detection of urine and fecal deposits by rodents, key ethological behaviors that have traditionally been very poorly studied. The strength of evidence for the claim is solid, showing accuracy near 90% across several contexts. Training and testing for the specific contexts used by other experimenters, however, is probably warranted to make the model most relevant to the data that may be analyzed.

Abstract In many mammals, including rodents, social interactions are often accompanied by active urination (micturition), which is considered a mechanism for spatial scent marking. Urine and fecal deposits contain a variety of chemosensory signals that convey information about the individual's identity, genetic strain, social rank, and physiological or hormonal state.