This time it's easier and at the same time harder to evaluate. Below are some notable aspects I noticed:

• The suggestions mostly focused on locations and faunas/floras, which is even less diverse than last time. Or maybe it's due to the fact that the number of articles on those 2 topics in Vietnamese wiki is way higher compared to other topics? (so once we had bots creating tons of articles on these 2 topics)
• More "2" results are given, which is a absolutely better than last time.
• Some images are on topic, but on the other hand if I were to decide whether to put them in an article or not, I'd choose not to, because they are either low quality or not actually provide any useful information for the article (despite being on topic), e.g: Image/Article in English. In this case I'm hesitating whether to put a 0 or a 2... Please get back to me on this so I can fix my grade.
• Some images don't have enough information on the file (nor are used on any articles) so I cannot grade them.

So overall it's better, but I'm thinking it's because most of the suggestions are on locations and faunas/floras, and the matching rate for these 2 topics were already high the last time we evaluated. I'd love to have suggestions that filter out these 2 topics.

Also I want to point out a fact that some of the images only have captions in other languages rather than English or don't have a caption at all, and are not used on any articles, so it might be difficult for newcomers to really understand what those pictures are about and to evaluate whether those pictures fit in the articles or not.

In case my script from T260857#6402309 is useful: I created https://people.wikimedia.org/~urbanecm/growth-team/image-evaluation-sept-2020-T263374/ to make evaluation easier for me.

Used the page created by @Urbanecm_WMF , thanks!
Images evaluated for arwiki and frwiki:

• same results in both wikis,
• big improvement, many accurate images,
• images with 1 score can fit articles in some context, I was a bit severe.

{{done}}

In my case, I am unable to spot any topic bias (just like last time). The algorithm is much more accurate, basically the only mistakes were mistakes in the source data and disambig pages.

That leads me to a thought: When we ask newcomers to evaluate an image, and they say no, can we fix the source data on their behalf, to improve Wikimedia in each case? Removing a category/WD item assignment should be easy enough IMO. @MMiller_WMF, do you think that's a good idea to consider?

Quite a few animals, train stations (specifically in Japan), cities in China (the mainland), and otherwise diverse topics. One "1946 (Year)" page.

We have all languages covered and some comments. @MMiller_WMF, I let you check if this task is done. If so, please close it.