  We explore statistical properties of BPS q-series for 3d N=2 strongly coupled
supersymmetric theories that correspond to a particular family of 3-manifolds
Y. We discover that gaps between exponents in the q-series are statistically
more significant at the beginning of the q-series compared to gaps that appear
in higher powers of q. Our observations are obtained by calculating saliencies
of q-series features used as input data for principal component analysis, which
is a standard example of an explainable machine learning technique that allows
for a direct calculation and a better analysis of feature saliencies.
