Dear Alex, thanks for the reply.
Please note that my suggestion wasn’t meant as
‘I want this, so please make it happen’
but rather as a request and an attempt to understand if this is possible at all.
Here’s what I pictured:
a.) Count all observations of Taxon xyz where the CV suggestion was chosen as the initial ID - indicated by this symbol:
b.) Count the subset of above observations where subsequently
another user disagreed this is Taxon xyz
c.) define a threshold [= % of b)/a)] → if met, then the CV decides to not include this taxon in its suggestions
d.) include geography in above model (as with ‘seen nearby’ suggestions)
I haven’t written a line of code in my life, so I cannot provide any technical concept - the core idea was that if it can be tracked how often a taxon ID suggested by the CV receives subsequent disagreements, then this could become an automated learning process without the need of a curated ‘problematic species list’ (I contributed to Cassi’s thread myself by adding both species to the list and removing others which were successfully cleaned up). Or, in other words, the activities of IDers will be combined with the visual identification model.
Take for example the snail Succinea putris (see also the flag and comments)
Due to massive efforts, there are now almost no observations for this species in America, but a short time ago, there were more than 1,000 - with a constant influx of new ones, thanks to AI suggestions.
CV probably learned from European RG-observations, as the species likely not occurs in NA at all.
I imagine the CV could be trained that way:
At one cut-off date, in
'continent: North America' there were 8,346 initial
'taxon: Succinea putris-IDs where the observer chose the AI-suggestion, and 8,344 of those have received subsequent disagreements, which is higher than the threshold of
xy% – so the AI will not suggest S. putris for North America (but might still do so for Europe, or in a future learning round).
I myself helped getting the flesh fly Sarcophaga carnaria
out of the CV pool by reducing the observations way below 100 - now there are almost no new observations on species level (the genus can generally only be IDed by genitalia).
However, with the amber snail that approach would not be possible due to many RG-observations in Europe, where they are probably correct. Plus, for the almost identically looking set of American Amber Snail species, probably internal genitalia structures are needed for reliable IDs, so it is unlikely that there will ever be enough observations to teach the AI alternative suggestions.
With the ‘self-critical’ AI model suggested here, such situations seem to be better manageable.
This is not a feature request, as this still a very rough draft, but maybe worth considering to pursue? Proof of concept would of course be needed