tor (expert/non-expert): this is effectively linearly interpolating the distributions per-pixel from their one-hot encoding (i.e. a vector of binary variables for each class label) to uniform probability. We used 0.2 for experts, and 0.3 for non-experts (i.e. ~82% confidence on the true class for experts and ~73% confidence on the true class for the non-expert. We note that these values approximately mirror the Non-Expert to Expert Consensus agreement as discussed in the Technical Validation section). This is akin to standard label-smoothing