  In this work, the empirical-Gramian-based model reduction methods: Empirical
poor man's truncated balanced realization, empirical approximate balancing,
empirical dominant subspaces, empirical balanced truncation, and empirical
balanced gains are compared in a non-parametric and two parametric variants,
via ten error measures: Approximate Lebesgue $L_0$, $L_1$, $L_2$, $L_\infty$,
Hardy $H_2$, $H_\infty$, Hankel, Hilbert-Schmidt-Hankel, modified induced
primal, and modified induced dual norms, for variants of the thermal block
model reduction benchmark. This comparison is conducted via a new meta-measure
for model reducibility called MORscore.
