  Effective field theories endowed with a nontrivial moduli space may be broken
down by several, distinct effects as the energy scales that are probed
increase. These may include the appearance of a finite number of new states, or
the emergence of an infinite tower of states, as predicted by the Distance
Conjecture. Consequently, the moduli space can be partitioned according to
which kind of state first breaks down the effective description, and the
effective-theory cutoff has to be regarded as a function of the moduli that may
abruptly vary in form across the components of the partition. In this work we
characterize such a slicing of the moduli space, induced by the diverse
breakdown mechanisms, in a two-fold way. Firstly, employing the recently
formulated Tameness Conjecture, we show that the partition of the moduli space
so constructed is composed only of a finite number of distinct components.
Secondly, we illustrate how this partition can be concretely constructed by
means of supervised machine learning techniques, with minimal bottom-up
information.
