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
Zheng et al. (2023), "Judging LLM-as-a-judge with MT-Bench and Chatbot Arena"
Monday build block
· Read it inside Monday's build block
Build-block reading · Zheng et al. (2023), "Judging LLM-as-a-judge with MT-Bench and Chatbot Arena", NeurIPS Datasets & Benchmarks, sections 1-3 (judge biases you must avoid in week 14)
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.”