What Improves When a Microscopy Encoder Is Updated?

Microscopy encoders support scientific measurements through classifiers, segmentation decoders and measurement rules. We study which update gains survive the choice to co-train a head, leave it unchanged, or fit it afresh to frozen features. In matched dense-encoder forks across ten seeds, absolute task weighting leads normalization by 4.92 percentage points of culture balanced accuracy after 512 updates through co-trained heads and 4.22 through unchanged source heads; fresh-head contrasts remain unresolved. The preference is therefore measurable as compatibility with an existing classifier. The co-trained–fresh interaction persists under fresh-optimizer and separate-clipping controls, but equal-budget per-arm learning-rate tuning removes the early absolute advantage. Later heads diverge in recipe ranking without meeting the prespecified criterion for a co-trained head hiding resolved accuracy loss. DINOv2-small does not reproduce the early preference at its default rates. SCOPE, a completed 4B-parameter microscopy model, supplies complementary positive evidence: an unchanged classifier improves from 0.6235 to 0.9798 on terminal features, and fresh fits also improve on the previously supervised culture task. On 23 held-out Allen plates, brightfield structure Dice is 0.312, exceeding a 37M-parameter scratch control by 0.056; most of this capability predates continuation. Spatial controls show how decoder capacity and feature access change the update contrast. On held-out larvae, improved counts coexist with reduced strict object overlap, while a validation-based retention gate has no consistent advantage on external reporter assays. Most follow-ups reuse inspected panels. The study makes encoder progress interpretable through the complete use: which readout changes, which labels and optimization it receives, and which biological measurement the resulting procedure supports.