Geomodel Hexagons Still Lacking Basic Error Checking

[einsum]

@rupertclayton

The benefit of including the geomodel is to broaden CV suggestions beyond just those visually similar species seen nearby and include additional other visually similar species that we might expect given the elevation

not exactly. the geomodel has another equally important job - it has to gatekeep the seemingly perfect CV suggestion from another continent from popping up suddenly as top-1

so under this precision-recall tradeoff and also training for elevations now equally in new geomodel (in not so perfect way but decent as whole), what the system is currently doing is “Visually similar AND expected nearby” and its not “Visually similar OR expected nearby” (maybe one of those current blatant misrepresentation on iNat - another being branch disagreement code and reality of UI that is pending for years) that I guess is probably stuck when changing the label from “Seen nearby” to “Expected nearby” when this geomodel is pushed. I am not a longterm user on iNat so you veterans has to correct me, but if this @kueda presentation is the original cause of that sentence then you can see how it was OR back then aligning with what you said but its AND now (see nearby injection in slide where chrysolepis is injected without CV prior and is the reason for OR)

@rupertclayton

But… there would also seem to be no disadvantage to automatically tweaking the hexagon map for a taxon to add in any cell with RG observations.

yes, the tweak should be done I agree but it cannot be done until we overhaul the geomodel assumptions in first place by possibly learning a new function and what counts as “automatic simple tweaking” is not so automatic when we look at real details of precision-recall tradeoffs overall on platform.

@rupertclayton

A model that pays attention to elevation range while always suggesting species that occur nearby seems to be better suited to real-world use by iNat observers and identifiers than one that ignores nearby records in favor of only trusting the elevation model it has built.

so yes we need a new learnable function and more importantly we badly need some more published model statistics (instead of one highly aggregated curves which to me is useless in these discussions) from inat to realise these benefits by tweaks across model versions to be caught by anyone.

@rupertclayton

Because elevation is only a partial guide, this will likely mean that the list of suggestions sometimes includes more incorrect options, and so long as that’s not too frequent the trade-off is worth it.

and we dont know this again, whether what trade offs are worth it. you are arguing only from that missed recall suggestion point that geomodel gatekeeped currently and by presenting it as perfect recall is good now, but my point in above new reply is we cannot say this on the top of head and assume it applies equally to entire inat level whether that gatekeeped suggestion is better or worse - when viewed in the lens of true precision and we are lack real numbers to prove such argument.