classification to save labor

I spent part of the day discussing with Valentina Tardugno (NYU) and Nora Eisner (Flatiron) the goals of a machine-learning classification that Tardugno is creating to help the PlanetFinders project. The deal is: Citizen scientists find candidate planets and (currently) a human (Eisner) has to vet them, to remove contamination by various sources of false positives. This turns out to be a hard problem! When problems are hard, it becomes critical to very precisely specify what you are trying to achieve. So we spent time discussing what, exactly, it is that Eisner needs from a classifier. Is it to find good planets? Is it to remove obvious contaminants? Are some contaminants more problematic than others? Is it to save her hours of wall-clock time? Etc.