Now the junior analyst meets the answer key. You build extract/agent.py with two brains, a frontier model through the API and a local Qwen on your 3050, plus a fake brain for tests. Then you do the thing that distinguishes an AI scientist from an AI enthusiast: you measure it per field, not as one flattering average. It will be very good at "organism" and bad at "working volume", and that unevenness is the finding. …
If you only do one thing this week: the F1 table, whatever it says
Statistical Rethinking 2024, lectures 1-2 (YouTube, from github.com/rmcelreath/stat_rethinking_2024) or Murphy, Probabilistic Machine Learning: An Introduction, ch. 2 — probml.github.io/pml-book/book1.html (75 min). Pick Rethinking if you want stories; Murphy if you want equations
By Sunday you should be able to say: “I can read my own F1 table aloud, say why the worst field fails, and explain why tuning on the gold set is dangerous.”