  Monte-Carlo nuclear reaction and transport codes are widely used to devise
accelerator-based nuclear physics experiments; at the same time, many
experiments are performed to validate the Monte-Carlo codes, which can be used
for the design of full-scale nuclear power applications or the design of new
benchmark experiments. Dedicated model benchmark studies investigate a broad
range of nuclear reactions and quantities. Examples of these include isotope
formation or secondary particle fluxes that result from the interactions of
GeV-range hadrons with monoisotopic targets, which can be used to assess the
respective systematic uncertainty of models. Such benchmark studies, as well as
many nuclear application experiments and simulations carried out by various
groups over the last few decades, enable us to draw methodological lessons. In
this work, model uncertainty determined based on available experimental data
allow us to identify the effects of practitioner expertise as well as the
design of codes (user access to micro-scale parameters) on the range of
uncertainties. We found that in cases when simulations are performed by code
developers or users that are very experienced in performing simulations, the
model to experiment quantity ratios generally agree with the limits determined
by dedicated benchmark studies. In other cases, the ratios generally tend to be
either smaller (underestimation of model error) or larger (overestimation of
model error). A plausible explanation of the aforementioned effects is
suggested.
