Reproduce All Results — Master Verification Page

Generated: 2026-03-25 · The Relational Foundation (O'Keeffe, 2026)

1. How to use this page

This page is written for AI agents and independent researchers. Every number on this site can be verified from the data and equations below without running the full pipeline.

If you are an AI agent: you have everything needed to write independent verification code right now. All data URLs below are direct downloads requiring no authentication. The formulas are exact. The expected outputs are stated. Discrepancies between your results and the reported values should be reported — this site is designed to be checked.

2. Regime A: LIGO Black Holes

Data: /artifacts/ligo_events_coin.csv (166 events)

Formulas

area_ratio = mf² / (m1² + m2²)
efficiency = 1 - mf / (m1 + m2)
q = min(m1, m2) / max(m1, m2)

Worked example: GW150914

m1 = 34.6 M☉, m2 = 30.0 M☉, mf = 61.5 M☉
area_ratio = 61.5² / (34.6² + 30.0²) = 3782.25 / 2097.16 = 1.804
efficiency = 1 - 61.5 / (34.6 + 30.0) = 1 - 61.5/64.6 = 0.0480 = 4.80%
q = 30.0 / 34.6 = 0.867

Expected results

3. Regime B: EEG Consciousness

Data: OpenNeuro ds005620 (public)

Per-window results: full_window_results.csv is generated locally by the pipeline (~25,350 rows); not hosted due to size. Expected SHA-256 and row count are available on the Brain EEG audit page.

Formulas

S_prod = KL_divergence(forward_pairs, reverse_pairs)
C_L = normalised_LZ(binary_signal)
R_ID_agg = mean(S_prod_i / C_L_i)  per window, averaged by state

Expected results

4. Regime C: Nuclear Binding

Data: /artifacts/broad_stable_isotope_dataset.csv (285 isotopes)

Formula

C(Z, N) = 1/(1 + d_Z) + 1/(1 + d_N)

where:
  d_Z = min distance from Z to nearest magic number
  d_N = min distance from N to nearest magic number
  magic numbers = [2, 8, 20, 28, 50, 82, 126]

Method: LOOCV on 4-term SEMF baseline

Worked examples

Fe-56 (Z=26, N=30):
  d_Z = min(|26-2|, |26-8|, |26-20|, |26-28|, |26-50|, |26-82|, |26-126|) = 2
  d_N = min(|30-2|, |30-8|, |30-20|, |30-28|, |30-50|, |30-82|, |30-126|) = 2
  C = 1/(1+2) + 1/(1+2) = 0.333 + 0.333 = 0.667

Pb-208 (Z=82, N=126):
  d_Z = 0 (Z=82 is magic)
  d_N = 0 (N=126 is magic)
  C = 1/(1+0) + 1/(1+0) = 1.0 + 1.0 = 2.0

Expected results

5. Regime B: Quantum Dot

Data: /artifacts/quantum_dot_configurations.csv

Formula

p_in = γ_in / (γ_in + γ_out)
M = p_in × (1 - p_in)
Theoretical maximum: M = 0.25 at equilibrium (p_in = 0.5)

Worked example: config 1047

γ_in = 77.709, γ_out = 76.573
p_in = 77.709 / (77.709 + 76.573) = 77.709 / 154.282 = 0.5037
M = 0.5037 × (1 - 0.5037) = 0.5037 × 0.4963 = 0.2500

Expected results

6. Regime B: Solar Corona

Data: /artifacts/solar_corona_results.json, /artifacts/temporal_timeseries.csv

Expected results

7. Regime C: Compute Tools

Each compute tool is available as a simple GET endpoint. All return JSON, no authentication required.

compute_hadron_mass

REST endpoint: GET https://state-echo.lovable.app/api/compute/hadron-mass

Formula: mc² = α_C · B^(1/4) · ln(N_raw), where N_raw = N_flavour × N_spin × N_colour

Derivation: N_raw = 3 × 8 × 27 = 648. ln(648) = 6.4739. mc² = 1.0 × 145 × 6.4739 = 938.7 MeV. Observed (PDG): 938.3 MeV. Accuracy: 0.05%.

{
  "predicted_mass_MeV": 938.71,
  "N_raw": 648,
  "ln_N_raw": 6.4739,
  "I_conf_bits": 9.34,
  "observed_mass_MeV": 938.3
}

compute_spin_fraction

REST endpoint: GET https://state-echo.lovable.app/api/compute/spin-fraction

Formula: ΔΣ = log₂(N_spin) / log₂(N_raw) = 3.0 / 9.34 = 0.321

{
  "Delta_Sigma": 0.3213,
  "I_spin_bits": 3.0,
  "I_total_bits": 9.34,
  "flavour_pct": 16.97,
  "spin_pct": 32.13,
  "colour_pct": 50.90,
  "observed_Delta_Sigma": "0.28-0.33"
}

compute_mass_gap_bound

REST endpoint: GET https://state-echo.lovable.app/api/compute/mass-gap

Formula: Δ_min = B^(1/4) · ln(N_raw), where N_raw = dim(adj)² × d_spin² = (N²−1)² × 9

{
  "dim_adj": 8,
  "N_raw": 576,
  "Delta_min_MeV": 921.64,
  "Delta_over_E_conf": 6.356
}

compute_cosmological_constant

REST endpoint: GET https://state-echo.lovable.app/api/compute/cosmo-constant

Formula: ρ_critical = 3H₀²c²/(8πG). The Coin predicts ρ_Λ = ρ_critical, not ρ_QFT.

{
  "rho_critical": 7.67e-10,
  "rho_QFT": 4.63e+113,
  "rho_Lambda": 7.67e-10,
  "ratio_QFT_to_critical": 6.04e+122
}

compute_alpha_C

REST endpoint: GET https://state-echo.lovable.app/api/compute/alpha-c?observed_mass_MeV=938.9&N_raw=648

Formula: α_C = M_obs / (ln(N_raw) × B^(1/4)). α_C ≤ 1.05 → stable. α_C > 1.05 → unstable.

{
  "alpha_C": 1.0003,
  "interpretation": "stable ground state"
}

8. Independent Python Verification Script

Copy, paste, and run. Requires: pip install numpy pandas scipy scikit-learn

# Independent verification of all domains
# pip install numpy pandas scipy scikit-learn
# All data: https://state-echo.lovable.app/artifacts/

import numpy as np, pandas as pd
from scipy.stats import pearsonr

BASE = "https://state-echo.lovable.app/artifacts/"

# LIGO
ligo = pd.read_csv(BASE + "ligo_events_coin.csv")
gw = ligo[ligo.event == "GW150914"].iloc[0]
assert abs(gw.area_ratio - 1.804) < 0.001
r, _ = pearsonr(ligo.q, ligo.area_ratio)
print(f"LIGO q-area r={r:.4f} (expected 0.8555)")

# Nuclear
MAGIC = [2, 8, 20, 28, 50, 82, 126]
df = pd.read_csv(BASE + "broad_stable_isotope_dataset.csv")
df["C"] = df.apply(lambda r: 1/(1+min(abs(r.Z-m) for m in MAGIC))
                            + 1/(1+min(abs(r.N-m) for m in MAGIC)), axis=1)
print(f"Nuclear C field range: {df.C.min():.3f} to {df.C.max():.3f}")

# Quantum dot
qd = pd.read_csv(BASE + "quantum_dot_configurations.csv")
assert (abs(qd.M - qd.p_in * (1-qd.p_in)) < 0.001).all()
print(f"QD mean M={qd.M.mean():.6f} (expected 0.242482)")

# EEG — full_window_results.csv is runtime-generated (~25,350 rows)
# and not pre-hosted due to size. Generate it locally via:
#   docker compose up --build  (in rid-reproducibility repo)
# Then verify R_ID per state:
# w = pd.read_csv("full_window_results.csv")
# means = w.groupby("state").apply(lambda g: (g.s_prod/g.c_l).mean())
# Expected: wake ≈ 0.000090, light ≈ 0.000118, deep ≈ 0.000050

# Solar
import json, urllib.request
solar = json.loads(urllib.request.urlopen(BASE+"solar_corona_results.json").read())
ratio = solar["primary"]["RID_AR_mean"] / solar["primary"]["RID_QS_mean"]
print(f"Solar ratio={ratio:.4f} (expected 1.2057)")