JOB138 PREFREEZE DEVELOPMENT RECORD

The initial fitted deployment model matched all590development fields across
all7compact channels. No model/weight selection or parameter tuning followed
that result; seed138001 was declared beforehand. All3resampled fits have
identical effective runtime coefficients/operators/thresholds, with different
training-count metadata. Eight-turn tests on all590development sources stayed
exact at eachturn for all3seeds.

Independent audit caught a capacity robustness defect under deliberately
corrupted birth-gate weights: a64cell source could index the unavailable65th
slot. The failure is retained in audit-prefreeze-capacity-failure.json. The
runtime now independently enforces64cell capacity and only creates a membrane
when no membrane is present. These are disclosed grammar/structural safeguards.

Initial JavaScript deep equality failed143of590fixtures because negating layer0
produced JavaScript-0 while NumPy integer output carried0. The complete failure
report is retained. Emitted integer layer values now normalize zero. All590
fullfields, birth choices, repairs and diagnostics pass Python/JS exact parity.

UI review found the fallback birth address could violate the nNN grammar when
the default appended-slot name already existed. Both runtimes now select the
next unused valid n01..n99 address. Independent renamed/gapped-name fixtures
and the41UItests pass. Existing addresses remain unchanged.

Before freeze the planned480hard scenes were improved by replacing60duplicate
empty scenes with60explicit occupied-scaffold boundary scenes. There remain
20empty stress cases. The designed predicted clearances25/26/27 exercise the
known26-unit spacing threshold; they are not unseen-rule discovery. This
change preceded any final source generation and did not alter the model.

Code readability cleanup removed a dead conditional and corrected a training
comment: the membrane-drift labels explicitly use the known0.025teacher term,
rather than claiming a trace capture. Module wording and REPRODUCE likewise
identify structural constants/rule-derived supervision alongside fitted values.
No executed numerical training behavior changed in this cleanup.