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RefineErrorModel fits the variance model a*sigma^2 + (b*<I>)^2 to the binned *median* of the squared symmetry-mate deviations, chosen for robustness. But a single deviation squared over its variance is chi-square(1)-distributed, whose median is only 0.4549 of its mean, so the fit was calibrating the variances to 0.4549x their true value: the merged sigmas came out ~1.48x too small and the achieved reduced chi^2 was 1/0.4549 = 2.2, not 1. The error model was internally well-behaved (flat chi^2 across resolution) but globally over-confident, which inflated ISa (=1/b) by ~1.48x and made the exported sigmas too optimistic for downstream weighting / French-Wilson. Divide the binned median by the chi-square(1) median (0.4549) to recover an unbiased estimate of the mean E[dev^2]=sigma^2, keeping the robustness of the median while targeting reduced chi^2 = 1. Also compute the achieved median reduced chi^2 (same normalization) and report it on the "Error model" line so mis-calibration can no longer drift silently. Verified: HEWL rotation a 0.588->1.292, b 0.052->0.077, ISa 19.1->12.9, chi^2 2.17->1.06; serial Jet8 ISa 1.0->0.7, chi^2 0.92. Relative ISa comparisons and all CC1/2/CCref/anomalous metrics are unchanged (sigma-independent or a common constant); only the absolute sigma calibration is corrected. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>