Embedding difference weighs the probabilities with center-embedding depths and then normalizes the values; since the measure is a weighted average of embedding depths rather than a probability distribution, µ EMB is not always less than 1 and the correspondence with Kullback-Leibler divergence (Levy, 2008) does not hold, so it does not make sense to take the logs.
fitting the best model by AIC would actually prune away some of the factors as relatively insignificant, but these smaller models largely accord with the significance values in the table and are therefore not presented.