Oliveira et al. qualitatively tested MSER (original, batch, and LLM variants) in grand cannonical Monte Carlo simulations and concluded that it out-performed methods including Chodera's maximum effective sample size heuristic, which they found prone to late truncation. However, we found that the original MSER method ("uncorrelated estimate") often severely underestimated the optimal truncation time, producing very large RMSEs (for example, see MDM2-Pip2 in Table ). A key difference between Oliveira et al.'s data and ours is that the initial transient is fairly hidden by the high variance of our data (see Figure for the non block-averaged time series). It is likely important to account for autocorrelation to effectively recover the trend from the noise. This is not the case for Oliveira et al.'s data, where reasonable truncation times appear to be estimated without accounting for autocorrelation, avoiding the associated risk of late truncation. Hence, the optimal truncation point selection heuristic will likely depend on the properties of the data. However, as discussed below, the N n 0 window method remains a good choice for several variants of our synthetic dataset.