exceptionally small. honestly, i’m not sure why anyone would or should bother to analyze this kind of difference.
you could hypothesize that the difference here comes from bad computer vision suggestions, but if you look at the bigger picture, it seems to me like a better hypothesis would be simply that European identifiers are identifying proportionally more observations than their USA peers (as evidenced by the higher research grade ratio for Europe), meaning that it would not be unexpected that they would surface slightly more Mavericks in the Needs ID pile.
@DianaStuder talked about “Pre-Mavericks”, and if there were more “Pre-Mavericks” in the European set, you would expect the European “Leading” IDs to be proportionally higher than the those in the USA set of Needs ID observations, but the USA “Leading” IDs are actually proportionally higher. this would seem to contradict or at least not support your broader hypotheses that European CV suggestions are disproportionately worse, leading to lower data quality in Europe.
i’m not going to try to dig into either of these hypotheses, since i don’t think it’s worth my effort. but you’re welcome to do your own analysis.
here i agree with your first point in that it doesn’t look to me like there’s currently a problem in Europe (which is why i don’t understand why you think a “solution” needs to be implemented for a problem that doesn’t exist).
on your second point, this strikes me as borderline fear mongering that new arrivals will mess things up for the rest of us. from my perspective, to the extent that new folks make newbie mistakes is to be expected, but given proper onboarding, any negative impact should be minimal and temporary.
but if you dig into this and just think about how it would practically go down, you would realize that most students and new bioblitzers would be using iNat app on their mobile devices to record observations not the web uploader, and most people using the iNat app would get locations automatically captured via the iNat app or the mobile device camera app.
i don’t know why anyone would waste effort doing an A/B test on this kind of proposed change. just looking at the easily available metrics, you can already see that a different flow for the web uploader to try to improve computer vision suggestions is likely going to have near zero impact on overall data quality.
if someone somehow can rationalize the change some other way, that’s fine, but just implement the change. don’t confuse things by making some folks have one flow while others get another, or worse, by presenting one flow some of the time and another flow the rest of the time. any potential insights from such a test are likely going to be very minimal – not worth the effort.
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i understand why folks might think that bad computer vision suggestions can have a negative impact on data quality. if you’ve identified a reasonable number of observations, you’ll have come across observations where it looks like folks just took whatever the computer vision suggested, without really thinking about it. these examples stick out in our memories, but if you really think about all the observations you identify, you’ll realize that the vast majority of identifications that are labeled as computer vision assisted are just fine.