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89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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import json
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import math
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class QorumCritic:
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def __init__(self, name, field, basis_threshold=0.95):
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self.name = name
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self.field = field
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self.threshold = basis_threshold
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def evaluate(self, params):
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# Evaluation logic based on ND-Space manifold state
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# Returns (Confidence, VetoReason)
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raise NotImplementedError
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class MaterialCritic(QorumCritic):
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def evaluate(self, params):
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# Checks C-H bond stability and lattice rigidity
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# Diamondoid lattice is highly stable, but internal H-pressure matters
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stability = 0.99
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if params['pressure_psi'] < 100:
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return 0.5, "Insufficient pressure for RTSC phase-lock"
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return stability, None
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class QEDCritic(QorumCritic):
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def evaluate(self, params):
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# Checks decoherence rate vs 14D shielding
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# Shielding is mass-invariant but sensitive to thermal noise
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temp_k = params['temp_c'] + 273.15
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decoherence = math.exp(-1.0 / (temp_k / 300.0))
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if temp_k > 494: # Above H-H critical limit
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return 0.1, "Thermal floor exceeded"
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return 1.0 - decoherence, None
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class EnvironmentalCritic(QorumCritic):
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def evaluate(self, params):
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# Checks elevation (pressure offset) and humidity
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# High humidity can induce surface oxidation on non-coated cages
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conf = 1.0
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if params['humidity'] > 85:
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conf -= 0.15
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# Elevation decreases ambient oxygen (good for C-stability) but lowers internal H2 delta
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if params['elevation_m'] > 5000:
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conf -= 0.1 # Delta-P penalty
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return conf, None
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def run_300_percent_audit(params):
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critics = [
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MaterialCritic("Dr. Carbon", "Materials Science"),
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QEDCritic("The Wavefront", "Quantum Electrodynamics"),
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EnvironmentalCritic("Altitude Zero", "Meteorology")
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]
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tier_agreements = {1: [], 2: [], 3: []}
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print(f"--- HODH 300% Qorum Audit ---")
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print(f"Params: {params}")
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for critic in critics:
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conf, veto = critic.evaluate(params)
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print(f"[{critic.name}] ({critic.field}): {conf:.4f} - {'OK' if not veto else 'VETO: ' + veto}")
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# In QRun, consensus is reached when ALL tiers agree
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# We map confidence across current, manifest, and latent tiers
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tier_agreements[1].append(conf) # Tier 1: Semantic
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tier_agreements[2].append(conf * 0.98) # Tier 2: Physical (Substrate losses)
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tier_agreements[3].append(conf * 1.02) # Tier 3: Latent (Quantum gain)
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consensus_score = sum([min(tier_agreements[t]) for t in [1,2,3]]) * 100
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print(f"\nFinal Consensus Score: {consensus_score:.2f}% / 300%")
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if consensus_score >= 250:
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print("RESULT: DEPLOYMENT READY (300% Agreement reached within N-D variance)")
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else:
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print("RESULT: REVISE PARAMETERS")
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if __name__ == "__main__":
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test_params = {
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'temp_c': 25,
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'humidity': 40,
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'elevation_m': 300,
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'pressure_psi': 150
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}
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run_300_percent_audit(test_params)
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