For every train, there is a fixed number of tickets per price category. So sometimes, you can still find cheap tickets ("super sparpreis") a day before because thag specific train didn't have many bookings:)
The scope is a bit different. The study uses an LLM to interpret pose estimation data and describe the behavior in each frame. The output is text which can be used to create embeddings of behavior. As someone who works in ethology, that's a clever (but maybe expensive) idea.
I think the author could use something similar. With multi-person pose estimation models.
Since its text, especially text with links to other articles, there is no need for tags.
If I had a clue how to do this (sorry, just a neuroscientist), I would probably create "communities" of pages on a network graph and weight the traversal across the graph network based on pages that the person liked (or spend X time on before).
https://www.nature.com/articles/s41586-026-10466-y