[apatil]

In hindsight this comment was too short. Clarifying some points:

By "This makes sense", I meant that this kind of thing can happen; as more data are gathered, the Bayesian probability of a candidate value can increase and then suddenly decrease. Here's a Colab notebook demonstrating the general phenomenon: https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_...

"Calibration" in this context means "statistical consistency between distributional forecasts and observations" in the words of https://sites.stat.washington.edu/raftery/Research/PDF/Gneit... . If the model's early forecasts predict impact with probability >3% for a class of objects that end up impacting with frequency much less than 3%, then the model is not well calibrated with respect to its early forecasts for those objects.

Based on the GP, it sounds like these early impact "probabilities" are no one's subjective (Bayesian) probability of impact because people who are closely familiar with this model know it is not well calibrated. The reported probabilities may still be useful to them as indicators or flags. However, those of us who are _not_ closely familiar with the model have found it confusing to see things that are not really probabilities reported as probabilities.