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The difference was, it appears, due to the different implementation of Random Forests in R and Cascading Pattern (as well as openscoring which I tried later) with respect to ties in the tree voting - i.e. when an even number of trees are built (say, 500) and exactly half classify an application as Good, and the other half as Bad, the handling of those situations differs. Solved it by growing and odd (501) number of trees.
I think the most likely explanation is that the two libraries do not quite support TreeModel in PMML in the same way. Perhaps one only supports a subset of features, and ignores ones it does not understand. This could cause different scoring.
I'd also double check that upstream parsing code is the same in both cases. Maybe a missing value is treated differently upstream.