I have read the other review and author's rebuttal for that.

- Please define "calibration" as it has different definitions. For example, see https://en.wikipedia.org/wiki/Calibration_(statistics) and https://doi.org/10.1214/23-BA1404

- I'm also concenred with use of KL-divergence. I don't think author's rebuttal on this is sufficient. As the authors have used only uniform prior, the measure used is equivalent to entropy. Entropy is sensible measure for sharpness of the distribution. Instead of talking about KL-divergence, I suggest the authors would talk about entropy and use entropy also in case of non-uniform priors. Using entropy would focus on maximizing the sharpness of the posterior, while using KL can lead also maximal shift of the posterior.

- I found the Figure 2 and 3 schematics confusing, as by first look it looks like information flows from priors to likelihoods, which doesn't make sense. I don't have good suggestion how to change them, but wanted to mention this if the authors would have other ideas.

- The author's mention which MCMC algorithm is used, so the authors could also mention which convergence diagnostics were used from ArviZ package

- Explicitly define the prior in case study, now it seems based on the plots that it's unifrom, but the priors were not explicitly defined in the text. This make huge difference for the use of KL, as it's then equivalent to entropy which measures just the sharpness. Without explicitly mentioning the prior in the text, it takes more time and effort from the reader to see what has been actually used.

- Otherwise the paper is mostly clear and easy to follow and I don't have further comments