eLife assessment
This manuscript presents a valuable 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 their claim, however, that the method provides "easy, efficient, and unbiased spatiotemporal analysis of scent marking during behavioral experiments" is incomplete. In particular, there were concerns about the generalizability of the approach, the relatively limited detection capabilities of the method, and a lack of rationale for specific design choices. This manuscript could be of interest to researchers in animal behavior, neuroscience, and automated animal tracking.