Finite Reclosure and Conditional Gaussian Universality

This source-routed technical whitepaper reconstructs the precise relationship
between finite transverse DBA, physical probability aggregation, quadratic
series accommodation, and Gaussian or Gaussian-attracting scalar readouts in
the LST/MASSIVE program. It separates finite recoverable reclosure from any
probability law; distinguishes exact Gaussian stability, Gaussian projection,
maximum-entropy selection, and central-limit attraction; and qualifies every
law by its physically admitted compatibility support. The paper derives the
full-product and affine constrained Gaussian branches, identifies hidden
kernel fibers and their observable non-identifiability, states the
infinite-dimensional trace-class normalization cap, and gives a finite penta
control that preserves rather than deletes local contrasts. Three-chart LST
coherence is retained only as an optional equalizer specialization and does
not itself select a measure, readout, or bell curve. The result is a
reconstructible publication route from source architecture to conditional
probability consequences without promoting a scalar shadow into its cause.