The example above with the amber snail Succinea is a good one, and I said it in other threads and will probably repeat it in the future: what I consider the most efficient way is the CV to learn taxa which are difficult to ID by factoring in disagreements.
My wishlist:
- change the wording
<<we are PRETTY SURE>>
to something like
<<the COMPUTER ALGORITHM suggests>>
(because who at all is ‘we’ in the first place and how sure is ‘pretty sure’?) - have an onboarding process where at least once there is a pop-up when using a CV suggestion for the first time describing how to use it carefully
- and most importantly: a self-reflecting CV learning process
One major point: it will simply not always be possible to provide enough observations of similar species to have the CV learn the differences - because in many Arthropod taxa there is just no way to tell the species apart on photos (at least on lower resolution cellphone photos).
We had the ‘famous’ situation with the flesh fly Sarcophaga carnaria, which only got resolved not because there were more photos of other species so that the CV could learn the difference, but merely because there was a joint effort to push all the observations back on a higher level to have it below the threshold before the next learning round.
But what would happen when some experts now will upload enough correctly IDed S. carnaria (including both in situ photos and microscopy images)? The CV will again start to suggest this one species for all the blurry flesh fly photos posted on the side.
Similar situation with the Amber Snail Succinea: most species cannot be IDed on photos, but with my suggested process, provided enough disagreements, the CV could learn to be more careful with species suggestions for this taxon in North America, and there would be no need to provide the algorithm with alternative suggestions