  The frog model is an interacting particle system on a graph. Active particles
perform independent simple random walks, while sleeping particles remain inert
until visited by an active particle. Some number of sleeping particles are
placed at each site sampled independently from a certain distribution, and then
one particle is activated to begin the process. We show that the recurrence or
transience of the model is sensitive not just to the expectation but to the
entire distribution. This is in contrast to closely related models like
branching random walk and activated random walk.
