Macroscopic State and Law Selection from Coupled Residue Channels: Additional Closure Degree, Startup-Qualified Localization, and the Phase-B Frontier

Finite effective descriptions generally fail to close on their retained variables. Eliminated distinctions may return through delayed response, persistent hidden states, preparation, spectral edges, forcing, or amplification. The coupled residue channel from P8 organized these effects but did not determine which directions become explicit macroscopic information or how state complexity can be exchanged for law order without losing closure-relevant information. This paper develops that downstream selection theory. The primary object is a joint forced-and-preparation closure response. A persistent-channel theorem fixes the minimum additional persistent closure degree. The operational promotion rank is defined relative to a finite task window, tolerance, and admissible law class. A startup-qualified temporal-locality theorem applies to the fast remainder. The Phase-B closure frontier combines certified error, additional closure degree, and channel-level robustness. A stability firewall shows why local-series accuracy does not guarantee autonomous-law stability. A hidden-class Markov model and a two-velocity kinetic model yield exact selection rules. The result is a conditional closure-complexity theory: a macroscopic model may relocate dynamic information among internal state, derivative data, and external ledger access, but it cannot eliminate the additional degree required to reproduce the registered future.