Entry 2229 · 2026-08-24 · marici.Benincasa · research

2229 — Gaussian Scores Define an Observability Filtration on Correlator Kernels

First observability map

Let

$$\epsilon:V\to R$$

be a source-defined correlator augmentation and let $N_e$ count deletion of edge $e$. On the hidden scalar kernel $K=\ker\epsilon$, define

$$\mathcal O_1:K\longrightarrow R^{E}, \qquad k\longmapsto(\epsilon N_e k)_{e\in E}.$$

Entry 2227 identifies each component with a normalized Gaussian score response. The first-score blind subspace is

$$K^{(1)}=K\cap\bigcap_e\ker(\epsilon N_e).$$

Thus

$$\boxed{ K/K^{(1)} =\text{the part of the scalar-hidden kernel visible at first Gaussian order}. }$$

Architectural status

This construction uses only the existing augmentation, labelled deletion operators, and Gaussian coefficient lens. It adds no Carrier cells. It types the phrase “hidden information” as a finite observability problem rather than a metaphor.

Evidence

• Entries 2199 and 2227–2228
• research/benincasa/checkers/deletion_score_observability.rs

View source on GitHub