We may further employ the bridge variables in conjunction with the probability of AMP generation in a single PC (Fig. ) to aid in the interpretability of the models. In our case models demonstrating a monotonic linear increase in probability, especially those reaching a prediction probability of > 0.6 (128-dimensional AAE, 64-dimensional RNN, 64-dimensional RNN-Attention, 64 and 128-dimensional Transformer and 32 and 64-dimensional WAE models) are arguably the easiest to interpret, since we now essentially have a single linear mapping from the latent space to AMP probability, which for the RNNs and WAEs are comparatively non-distorted from the original space (cf. Sec. 3.4).