SHAP interpretations from zero-adjusted gamma model

[p-schaefer]

I am trying to understand the SHAP outputs from a Zero-Adjusted Gamma. I'll outline my questions below:

1. SHAP scores should sum to the models predictions. In the LSS context for a ZAG, they should sum to the concentration, rate, and gate shape parameters that are output from predict(..., pred_type="parameters"), correct? I find this is not quite the case when I look at the outputs of shap.TreeExplainer(). So I assume there is some transformation happening. Could someone elaborate on what those transformations are, and what they look like.
2. This one is maybe more general, but just in terms of interpreting the SHAP values for the shape distribution parameters, in the context of the transformations. If an observation's feature has a negative SHAP score for the gate parameter, that would imply it is, on average, decreasing the probability of a 0 outcome, and a positive SHAP score would imply it is increasing the probability of a 0 outcome. Is that interpretation correct?
3. In a normal Gamma distribution, the mean can be described by multiplying concentration and rate (I think). Can multiplying the SHAP scores for concentration and rate be used as a high level descriptor of the predictors effects on the non-zero mean part of the distribution? And, how does the transformations affect the results of that?

Thank you!