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module module moderate

IndisputableMonolith.Climate.PredictabilityFromJCost

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This module defines forecast J-cost on the uncertainty growth ratio together with horizon predicates and a predictability certificate for climate applications in Recognition Science. Researchers applying RS to climate modeling would cite the forecastCost construction and its elementary properties. The module imports Cost and Constants then supplies direct definitions plus basic lemmas on non-negativity and symmetry.

claimLet $r$ be the uncertainty growth ratio. Define forecastCost$(r) := J(r)$ where $J$ is the J-cost. Introduce predicates IsPastHorizon, IsWithinHorizon, PredictabilityThreshold and the certificate ClimatePredictabilityCert.

background

Recognition Science obtains all physics from a single functional equation whose cost function J satisfies the Recognition Composition Law J(xy) + J(x/y) = 2J(x)J(y) + 2J(x) + 2J(y). The imported Constants module fixes the RS-native time quantum τ₀ = 1 tick. The imported Cost module supplies the J-cost primitive used here to forecast uncertainty growth in the climate domain.

proof idea

This is a definition module, no proofs.

why it matters in Recognition Science

The module supplies the core objects ClimatePredictabilityCert and forecastCost that certify predictability within horizons using J-cost forecasts. It extends the general Cost framework into the climate domain and prepares the ground for downstream applications of the eight-tick octave and phi-ladder mass formulas.

scope and limits

depends on (2)

Lean names referenced from this declaration's body.

declarations in this module (12)