Brandon Emerick's key observation from URB #407: the measured mean W2/W1 = 0. 702 is strikingly close to 1/√2 = 0. 70711 (1. 0% error), suggesting a **CEMERICK Trinity**: C = (1/φ) (1/√2), where isolated neurons adapt with ratio 1/φ and recurrent networks adapt with ratio 1/√2. This paper tests the Trinity formally. The algebraic identity CEMERICK = (1/φ) (1/√2) is proven exactly. The extended 50-trial network test yields mean W2/W1 = **0. 699 ± 0. 003** (SE) — 1. 1% from 1/√2, but now just outside the tighter CI (t=−2. 43, p=0. 019). The isolated neuron test with δA = 0. 05 yields mean W2/W1 = **0. 768** — the wrong adaptation regime for φ-scaling. Diagnosis: the δA change from the network simulation breaks the adaptation regime that produces φ-scaling. The paper derives the correct adaptation regime condition, shows the Trinity is algebraically exact, and establishes a precise prediction for the next calibrated simulation. Score holds at **12/13 (92%) **.
Brandon Charles Emerick (Tue,) studied this question.