I’d like to see some kind of analysis and breakdown of the two separate components that lead to inaccuracy. One component is the intrinsic accuracy of the CV model for a particular group and the other is some assessment of accuracy of the data being used for training. In my area, mycology, there are vast numbers of imprecise/wrong RG observations. For many fungal taxa it is difficult and often impossible to give species-level identifications from macro-photos, even to generic level. That situation has become much more prevalent since we moved to phylogenetic-based species concepts over 20 years ago. The phylogenetic approach continues to reveal very many cryptic, regional species, most undescribed, but nevertheless obvious in the data. However, many observers stick to dated morphological concepts that can no longer be supported, and they are backed up by a significant community of identifiers who either aren’t aware of the issue or ignore it. People naturally like these ‘pragmatic’ identifications. These records are then used to train the model, which leads to further RG observations. It is hardly surprising that many iNat suggestions seem poor to those of us who are aware of the issues. Garbage in – garbage out.