On the contrary, the top performer posterior based metric, variability, is more likely to overestimate topic quality by assigning high ratings to relatively bad topics (dots with low human scores tend to be below the green line), but it performs relatively well for high-quality topics.
when we combine all the metrics in a supervised way, the topic quality estimation becomes more accurate, especially on 20NG corpus (i.e.