Same framework, applied to Nuclear

The framework's value lies in its universality across disparate domains. The brake operator \(\mathcal{B}\), dispersion \(\mathcal{S}\), consensus \(\mathcal{M}\), spectral primitive \(\mathcal{P}\), anti-shadow detector \(\mathfrak{A}\), and scope-reporter \(\mathscr{A}\) — together with Theorems 1–13 — are applied here exactly as on every other domain. Source code: github.com/senuamedia/uniformity. No per-domain calibration. No imported threshold. No bespoke fit.

What the framework provides for nuclear physics

The framework's contribution here is a methodology demonstration: the same residual-aggregation pipeline can extract and aggregate small residuals across thousands of cells with no tuning. Applied to AME2020 with the prediction \(\mathrm{BE} = \Delta m\) (from \(E = mc^2\)), residuals are consistent with zero to \(\sim 10^{-9}\) relative precision.

Headline results (catalogue instance 6)

  • 2,545 isotopes evaluated (\(A \ge 4\), stable / long-lived).
  • Median residual \(3 \times 10^{-9}\), max \(1 \times 10^{-7}\).
  • Honest caveat through \(\mathscr{A}\): AME tabulates BE from mass via \(E = mc^2\); this verifies internal consistency, not independent confirmation. Rigorous independent test would use Rainville-style direct calorimetry.

Experiments

Script: domains/nuclear/experiments/emc2_test.py.

Framework reading

The application demonstrates the framework's measurement pipeline at the precision limit and serves as a methodology proof-of-concept: the pipeline can extract and aggregate small residuals across large datasets with no domain-specific tuning. The framework's honest scope (Theorem 12) is exercised explicitly via \(\mathscr{A}\): the test is internally consistent at \(10^{-8}\), but not an independent confirmation of mass–energy equivalence.