The cluster's first run was gated against a baseline measured on the Spark.
The same weights score lower there (F1 0.840 vs 0.861, mask score 0.706 vs
0.757 on the 203 gold frames), so the gate failed a model that beats the
active one on F1 in 98.7 % of bootstrap draws when both are measured on one
machine.
Gold reports now record the environment (ultralytics, torch, host). When the
active model's report comes from other versions, train_run.py measures its
weights again in the job and gates against that; the predictions feed the
paired bootstrap. Without the weights there is no verdict and no promotion.
The SLURM scripts pull the active model's weights.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
