MMP.12:6 - Bias-Annotation

The quadratic case makes sensitivity and shrinkage calculable. It does not make quadratic penalties appropriate for every unknown. A smoothness preference can remove discontinuities; a sparsity preference depends on the representation; a learned preference can lose features absent from its construction cases.

The method treats the subject grounds of additional structure as a separate contribution. Its mathematical examples establish consequences of stated premises, not the suitability of those premises for a particular physical or organizational situation. More consequential use can require stronger subject evidence or a validated target bound; ordinary use can stop at an already sufficient comparison.