BH Sensor Matrix · Live Europe + Cooling Automata

Sensor Field / σ(x,t) → T,H mesh — live

⟢ Sampling rate (×Hz)

4 Hz

⟁ BH mass (log₁₀ kg)

10²² kg

Black-Hole Matrix

N_sensor—

Σ Field entropy H(T)—

Bekenstein N_BH—

Margin (orders)—

H_Q (lattice)—

Hawking T_H—

Page time τₚ—

ER=EPR latency—

t_scr—

Work spent (J)—

CCT Precipitate

CCT Event Log

Replicator Lattice — 100 questions

⟢ Phase filter

⟁ Coupling κ

⟁ Cooling τ

120

Triple-point ε

10⁻⁵

      Each cell = Qᵢ. Brightness = attention aᵢ ; colour = rheo phase. Daᵢ/dt = aᵢ(Δᵢ/Wᵢ − ⟨Δ/W⟩)(1−aᵢ). When ΣH̄_Q < ε, the lattice precipitates.
    

Cooling Automata — competing optimization strategies → replicator → composite intervention

⟢ Strategy count — N_strategies

9 active

⟁ Adaptive weight β (cost/efficacy)

0.8

⟢ Composite ΔT

—

— °C /min

⟁ Total cost

—

— J/s

◊ Strategy entropy H_S

—

— rewards issued

⟁ Avg T σ-field

—

— trend

▸ Hottest city

—

— °C

Composite Cooling Field

        Each pixel-color = strategies' weighted cooling potential at that lat/lng via inverse-distance-to-cities layer.
        The composite colouring equals Σᵢ aᵢ · gᵢ(x) where gᵢ is the i-th strategy's spatial kernel.
      

Performance vs Baseline

        Σ cyan = σ-avg T (real, from sensors).  Σ red dashed = no-action projection (diurnal+trend).  Σ green = composite-cooling projection.
      

        Composite cooling strategy evaluation: —
      