Goldbach Conjecture: Computational Pattern Detection Through Temporal Dynamics Framework

This paper applies the Temporal Dynamics Framework to Goldbach's Conjecture. The k-parameterisation and resonance function achieve 90.4% pattern fitting across 49,999 test cases. The paper reports an honest negative predictive result: prediction accuracy for individual Goldbach decompositions equals the baseline rate of 16.7% (exactly 1/6), providing no predictive power beyond random for the S(E) class distribution. The framework captures structural patterns in Goldbach representations but does not predict which specific decomposition will occur. All 13 verification tests pass. Paper 6 in the Temporal Dynamics Framework Research Series.