That said modeling relationships between taxonomic publications where the authors actually read the original species description, reviewed the type specimens, and thought about the actual species conscription is
appropriate.
This is the sort of things the Meta-Authorities would take into account when selecting a "follow-this-treatment" Usage-Instance for the preferred treatment of a name.
Also consider that a large proportion of specimens are misidentified, and it occurs to me that modeling things like species occurrences as if they are Puma concolor (Linnaeus, 1771) sensu stricto is probably not appropriate. At best they are something like (Felis concolor / Puma concolor) with some significant level of error.
GNA can't helpw ith that directly -- but it can help indirectly. Imagine a service that takes ever specimen in a given collection's database, and runs it against a mapping service as I described in the previous message. I can easily imagine a GIS-based algorithm that finds "outliers" -- that is occurrence records that appear to be outside the distribution based on the occurrence records from other sources. A clver/robust such algorithm could probably even discern whether the outlier likely represented a range extension (e.g. poorly-known species, plausible extansion), vs. a misidentification (e.g., well-known species and/or common misidentification).
This would lead to a set of flagged records from the collection that might be misidentified.
Rich