Random forest results on a held-out test set predicting the different types of cluster events a given cluster would experience in the next year, with the same features as in Table 1.

We achieve a micro-averaged F1 = 0.814 on our held-out test set, with a class-specific F1 = 0.818 for the class representing knowledge evolution (splits and merges). Per reported Gini feature importance of each independent variable, both interdisciplinarity scores are equally important, followed by number of weak members, then year. Note that the sort order of this table is identical to that of Table 1 to allow for more direct comparison of logistic regression coefficients to random forest feature importances.