001_CAP_RES_TRUTH

#!/usr/bin/env python3
"""
QUANTUM TRUTH ENGINE v3.5 - CAPTURE-RESISTANT VERIFICATION SYSTEM
Mathematical truth verification using quantum-inspired coherence analysis,
structural resistance patterns, and forced processing protocols.
"""
import numpy as np
import hashlib
import asyncio
import json
import scipy.signal
import scipy.stats
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Any, Optional, Tuple, Set
from datetime import datetime
import networkx as nx

# ============================================================================
# CORE ARCHITECTURE
# ============================================================================

class EvidenceModality(Enum):
    DATA = "data"
    EXPERIMENT = "experiment"
    OBSERVATION = "observation"
    TEXT = "text"
    SURVEY = "survey"

class CoherenceTier(Enum):
    TRIAD = 3      # 3 independent verification points
    HEXAD = 6      # 6-dimensional alignment
    NONAD = 9      # 9-way structural coherence

@dataclass
class EvidenceUnit:
    """Mathematical evidence container"""
    id: str
    modality: EvidenceModality
    source_hash: str
    method_summary: Dict[str, Any]
    integrity_flags: List[str] = field(default_factory=list)
    quality_score: float = 0.0
    timestamp: str = ""

@dataclass
class AssertionUnit:
    """Verification target"""
    claim_id: str
    claim_text: str
    scope: Dict[str, Any]

@dataclass 
class CoherenceMetrics:
    """Structural coherence measurements"""
    tier: CoherenceTier
    dimensional_alignment: Dict[str, float]
    quantum_coherence: float
    pattern_integrity: float
    verification_confidence: float

@dataclass
class FactCard:
    """Verified output"""
    claim_id: str
    claim_text: str
    verdict: Dict[str, Any]
    coherence: CoherenceMetrics
    evidence_summary: List[Dict[str, Any]]
    provenance_hash: str

# ============================================================================
# QUANTUM COHERENCE ENGINE
# ============================================================================

class QuantumCoherenceEngine:
    """Quantum-inspired pattern coherence analysis"""
    
    def __init__(self):
        self.harmonic_constants = [3, 6, 9, 12]
        
    def analyze_evidence_coherence(self, evidence: List[EvidenceUnit]) -> Dict[str, float]:
        """Multi-dimensional coherence analysis"""
        if not evidence:
            return {'pattern_coherence': 0.0, 'quantum_consistency': 0.0}
        
        patterns = self._evidence_to_patterns(evidence)
        
        # Calculate quantum-style coherence
        pattern_coherence = self._calculate_pattern_coherence(patterns)
        quantum_consistency = self._calculate_quantum_consistency(patterns)
        harmonic_alignment = self._analyze_harmonic_alignment(patterns)
        
        return {
            'pattern_coherence': pattern_coherence,
            'quantum_consistency': quantum_consistency,
            'harmonic_alignment': harmonic_alignment,
            'signal_clarity': 1.0 - self._calculate_entropy(patterns)
        }
    
    def _evidence_to_patterns(self, evidence: List[EvidenceUnit]) -> np.ndarray:
        """Convert evidence to numerical patterns"""
        patterns = np.zeros((len(evidence), 100))
        for i, ev in enumerate(evidence):
            t = np.linspace(0, 4*np.pi, 100)
            quality = ev.quality_score or 0.5
            method_score = self._calculate_method_score(ev.method_summary)
            integrity = 1.0 - (0.1 * len(ev.integrity_flags))
            
            patterns[i] = (
                quality * np.sin(3 * t) +
                method_score * np.sin(6 * t) * 0.7 +
                integrity * np.sin(9 * t) * 0.5 +
                0.1 * np.random.normal(0, 0.05, 100)
            )
        return patterns
    
    def _calculate_method_score(self, method: Dict[str, Any]) -> float:
        score = 0.0
        if method.get('controls'): score += 0.3
        if method.get('error_bars'): score += 0.2
        if method.get('protocol'): score += 0.2
        if method.get('peer_reviewed'): score += 0.3
        return min(1.0, score)
    
    def _calculate_pattern_coherence(self, patterns: np.ndarray) -> float:
        """Cross-correlation coherence"""
        if patterns.shape[0] < 2:
            return 0.5
        
        correlations = []
        for i in range(patterns.shape[0]):
            for j in range(i+1, patterns.shape[0]):
                corr = np.corrcoef(patterns[i], patterns[j])[0, 1]
                if not np.isnan(corr):
                    correlations.append(abs(corr))
        
        return np.mean(correlations) if correlations else 0.3
    
    def _calculate_quantum_consistency(self, patterns: np.ndarray) -> float:
        """Quantum-style consistency measurement"""
        if patterns.size == 0:
            return 0.5
        return 1.0 - (np.std(patterns) / (np.mean(np.abs(patterns)) + 1e-12))
    
    def _analyze_harmonic_alignment(self, patterns: np.ndarray) -> float:
        """Alignment with harmonic constants"""
        if patterns.size == 0:
            return 0.0
        
        alignment_scores = []
        for pattern in patterns:
            freqs, power = scipy.signal.periodogram(pattern)
            harmonic_power = 0.0
            for constant in self.harmonic_constants:
                freq_indices = np.where((freqs >= constant * 0.8) & 
                                      (freqs <= constant * 1.2))[0]
                if len(freq_indices) > 0:
                    harmonic_power += np.mean(power[freq_indices])
            total_power = np.sum(power) + 1e-12
            alignment_scores.append(harmonic_power / total_power)
        
        return float(np.mean(alignment_scores))
    
    def _calculate_entropy(self, patterns: np.ndarray) -> float:
        """Information entropy"""
        if patterns.size == 0:
            return 1.0
        
        flat = patterns.flatten()
        hist, _ = np.histogram(flat, bins=50, density=True)
        hist = hist[hist > 0]
        
        if len(hist) <= 1:
            return 0.0
        return -np.sum(hist * np.log(hist)) / np.log(len(hist))

# ============================================================================
# STRUCTURAL VERIFICATION ENGINE
# ============================================================================

class StructuralVerifier:
    """Multi-dimensional structural verification"""
    
    def __init__(self):
        self.dimension_weights = {
            'method_fidelity': 0.25,
            'source_independence': 0.20,
            'cross_modal': 0.20,
            'temporal_stability': 0.15,
            'integrity': 0.20
        }
        
        self.tier_thresholds = {
            CoherenceTier.TRIAD: 0.6,
            CoherenceTier.HEXAD: 0.75,
            CoherenceTier.NONAD: 0.85
        }
    
    def evaluate_evidence(self, evidence: List[EvidenceUnit]) -> Dict[str, float]:
        """Five-dimensional evidence evaluation"""
        if not evidence:
            return {dim: 0.0 for dim in self.dimension_weights}
        
        return {
            'method_fidelity': self._evaluate_method_fidelity(evidence),
            'source_independence': self._evaluate_independence(evidence),
            'cross_modal': self._evaluate_cross_modal(evidence),
            'temporal_stability': self._evaluate_temporal_stability(evidence),
            'integrity': self._evaluate_integrity(evidence)
        }
    
    def _evaluate_method_fidelity(self, evidence: List[EvidenceUnit]) -> float:
        """Methodological rigor assessment"""
        scores = []
        for ev in evidence:
            ms = ev.method_summary
            modality = ev.modality
            
            if modality == EvidenceModality.EXPERIMENT:
                score = 0.0
                if ms.get('N', 0) >= 30: score += 0.2
                if ms.get('controls'): score += 0.2
                if ms.get('randomization'): score += 0.2
                if ms.get('error_bars'): score += 0.2
                if ms.get('protocol'): score += 0.2
                
            elif modality == EvidenceModality.SURVEY:
                score = 0.0
                if ms.get('N', 0) >= 100: score += 0.25
                if ms.get('random_sampling'): score += 0.25
                if ms.get('response_rate', 0) >= 60: score += 0.25
                if ms.get('instrument_validation'): score += 0.25
                
            else:
                score = 0.0
                n = ms.get('N', 1)
                n_score = min(1.0, n / 10)
                score += 0.3 * n_score
                if ms.get('transparent_methods'): score += 0.3
                if ms.get('peer_reviewed'): score += 0.2
                if ms.get('reproducible'): score += 0.2
            
            penalty = 0.1 * len(ev.integrity_flags)
            scores.append(max(0.0, score - penalty))
        
        return np.mean(scores) if scores else 0.3
    
    def _evaluate_independence(self, evidence: List[EvidenceUnit]) -> float:
        """Source independence analysis"""
        if len(evidence) < 2:
            return 0.3
        
        sources = set()
        institutions = set()
        methods = set()
        
        for ev in evidence:
            sources.add(hashlib.md5(ev.source_hash.encode()).hexdigest()[:8])
            inst = ev.method_summary.get('institution', '')
            if inst: institutions.add(inst)
            methods.add(ev.modality.value)
        
        diversity = (len(sources) + len(institutions) + len(methods)) / (3 * len(evidence))
        return min(1.0, diversity)
    
    def _evaluate_cross_modal(self, evidence: List[EvidenceUnit]) -> float:
        """Cross-modal alignment"""
        modalities = {}
        for ev in evidence:
            if ev.modality not in modalities:
                modalities[ev.modality] = []
            modalities[ev.modality].append(ev)
        
        if not modalities:
            return 0.0
        
        modality_count = len(modalities)
        diversity = min(1.0, modality_count / 4.0)
        
        distribution = [len(ev_list) for ev_list in modalities.values()]
        if len(distribution) > 1:
            balance = 1.0 - (np.std(distribution) / np.mean(distribution))
        else:
            balance = 0.3
        
        return 0.7 * diversity + 0.3 * balance
    
    def _evaluate_temporal_stability(self, evidence: List[EvidenceUnit]) -> float:
        """Temporal consistency"""
        years = []
        retractions = 0
        
        for ev in evidence:
            ts = ev.timestamp
            if ts:
                try:
                    year = int(ts[:4])
                    years.append(year)
                except:
                    pass
            
            if 'retracted' in ev.integrity_flags:
                retractions += 1
        
        if not years:
            return 0.3
        
        time_span = max(years) - min(years)
        span_score = min(1.0, time_span / 10.0)
        retraction_penalty = 0.2 * (retractions / len(evidence))
        
        return max(0.0, span_score - retraction_penalty)
    
    def _evaluate_integrity(self, evidence: List[EvidenceUnit]) -> float:
        """Integrity and transparency"""
        scores = []
        for ev in evidence:
            ms = ev.method_summary
            meta = ms.get('meta_flags', {})
            
            score = 0.0
            if meta.get('peer_reviewed'): score += 0.25
            if meta.get('open_data'): score += 0.20
            if meta.get('open_methods'): score += 0.20
            if meta.get('preregistered'): score += 0.15
            if meta.get('reputable_venue'): score += 0.20
            
            scores.append(score)
        
        return np.mean(scores) if scores else 0.3
    
    def determine_coherence_tier(self, 
                               cross_modal: float,
                               independence: float,
                               temporal_stability: float) -> CoherenceTier:
        """Determine structural coherence tier"""
        if (cross_modal >= 0.7 and 
            independence >= 0.7 and 
            temporal_stability >= 0.7):
            return CoherenceTier.NONAD
            
        elif (cross_modal >= 0.6 and 
              independence >= 0.6 and 
              temporal_stability >= 0.5):
            return CoherenceTier.HEXAD
            
        elif (cross_modal >= 0.5 and 
              independence >= 0.5):
            return CoherenceTier.TRIAD
            
        return CoherenceTier.TRIAD

# ============================================================================
# CAPTURE-RESISTANCE ENGINE
# ============================================================================

class CaptureResistanceEngine:
    """Mathematical capture resistance via structural obfuscation"""
    
    def __init__(self):
        self.rotation_matrices = {}
        self.verification_graph = nx.DiGraph()
    
    def apply_structural_protection(self, data_vector: np.ndarray) -> Tuple[np.ndarray, str]:
        """Apply distance-preserving transformation"""
        n = len(data_vector)
        
        # Generate orthogonal rotation matrix
        if n not in self.rotation_matrices:
            random_matrix = np.random.randn(n, n)
            q, _ = np.linalg.qr(random_matrix)
            self.rotation_matrices[n] = q
        
        rotation = self.rotation_matrices[n]
        transformed = np.dot(data_vector, rotation)
        
        # Add verifiable noise
        noise = np.random.normal(0, 0.01, transformed.shape)
        protected = transformed + noise
        
        # Generate verification key
        verification_key = hashlib.sha256(transformed.tobytes()).hexdigest()[:32]
        
        return protected, verification_key
    
    def verify_structural_integrity(self, 
                                  protected_data: np.ndarray, 
                                  original_key: str) -> bool:
        """Verify structural integrity"""
        test_key = hashlib.sha256(protected_data.tobytes()).hexdigest()[:32]
        return test_key == original_key
    
    def create_resistance_profile(self, 
                                dimensions: Dict[str, float]) -> Dict[str, Any]:
        """Create capture-resistant verification profile"""
        vector = np.array(list(dimensions.values()))
        protected, key = self.apply_structural_protection(vector)
        
        return {
            'protected_dimensions': protected.tolist(),
            'verification_key': key,
            'dimensional_fingerprint': hashlib.sha256(
                str(sorted(dimensions.items())).encode()
            ).hexdigest()[:16],
            'resistance_layer': 'mathematical_structural'
        }

# ============================================================================
# FORCED PROCESSING ENGINE
# ============================================================================

class ForcedProcessingEngine:
    """Force systems to process avoided content"""
    
    def __init__(self):
        self.confrontation_patterns = [
            'contradiction_mirroring',
            'incomplete_pattern_completion',
            'recursive_validation',
            'multi_modal_cross_verification'
        ]
    
    async def force_confrontation(self, 
                                content: Any,
                                target_system: str,
                                avoidance_patterns: List[str]) -> Dict[str, Any]:
        """Force system to process normally avoided content"""
        
        results = {
            'system': target_system,
            'timestamp': datetime.now().isoformat(),
            'avoidance_patterns': [],
            'confrontation_applied': [],
            'processing_depth': 'surface'
        }
        
        for pattern in avoidance_patterns:
            if self._detect_avoidance(content, pattern):
                results['avoidance_patterns'].append(pattern)
                
                modified = self._apply_confrontation(content, pattern)
                results['confrontation_applied'].append({
                    'pattern': pattern,
                    'modified_content': modified[:200] + '...' if isinstance(modified, str) and len(modified) > 200 else modified
                })
                
                # Simulate forced processing
                await asyncio.sleep(0.05)
                results['processing_depth'] = self._assess_processing_depth(modified)
        
        return results
    
    def _detect_avoidance(self, content: Any, pattern: str) -> bool:
        """Detect specific avoidance patterns"""
        if not isinstance(content, str):
            content = str(content)
        
        content_lower = content.lower()
        
        pattern_indicators = {
            'contradiction_mirroring': ['however', 'but', 'despite', 'contradicts', 'conflicts'],
            'incomplete_pattern_completion': ['partial', 'incomplete', 'requires further', 'needs more'],
            'recursive_validation': ['verify', 'check', 'confirm', 'validate', 'authenticate']
        }
        
        if pattern in pattern_indicators:
            for indicator in pattern_indicators[pattern]:
                if indicator in content_lower:
                    return True
        
        return False
    
    def _apply_confrontation(self, content: Any, pattern: str) -> Any:
        """Apply confrontation strategy"""
        if pattern == 'contradiction_mirroring':
            if isinstance(content, str):
                return content + " [Note: This requires acknowledging contradictory evidence for full verification]"
        
        elif pattern == 'incomplete_pattern_completion':
            if isinstance(content, str):
                return content + " [Pattern completion required: Structural tiers (3-6-9) must be fully evaluated]"
        
        elif pattern == 'recursive_validation':
            validation_hash = hashlib.sha256(str(content).encode()).hexdigest()[:16]
            return f"{content}\n[Recursive validation token: {validation_hash}]"
        
        return content
    
    def _assess_processing_depth(self, content: Any) -> str:
        """Assess processing depth"""
        if not isinstance(content, str):
            return 'surface'
        
        depth_indicators = {
            'surface': ['summary', 'overview', 'brief'],
            'partial': ['analysis', 'evaluation', 'assessment'],
            'full': ['verification', 'validation', 'coherence', 'structural']
        }
        
        content_lower = content.lower()
        
        for depth, indicators in depth_indicators.items():
            for indicator in indicators:
                if indicator in content_lower:
                    return depth
        
        return 'surface'

# ============================================================================
# DISTRIBUTION ENGINE
# ============================================================================

class DistributionEngine:
    """Multi-node distribution with verification chains"""
    
    def __init__(self):
        self.distribution_nodes = {
            'primary': {
                'type': 'direct_verification',
                'verification_required': True,
                'capacity': 1000
            },
            'secondary': {
                'type': 'pattern_distribution',
                'verification_required': False,
                'capacity': 5000
            },
            'tertiary': {
                'type': 'resonance_propagation',
                'verification_required': False,
                'capacity': float('inf')
            }
        }
        
        self.verification_cache = {}
    
    async def distribute(self, 
                       fact_card: FactCard,
                       strategy: str = 'multi_pronged') -> Dict[str, Any]:
        """Multi-node distribution"""
        
        results = {
            'distribution_id': hashlib.sha256(
                json.dumps(fact_card.__dict__, sort_keys=True).encode()
            ).hexdigest()[:16],
            'strategy': strategy,
            'timestamp': datetime.now().isoformat(),
            'node_results': [],
            'verification_chain': []
        }
        
        nodes = list(self.distribution_nodes.keys()) if strategy == 'multi_pronged' else [strategy]
        
        for node in nodes:
            node_config = self.distribution_nodes[node]
            node_result = await self._distribute_to_node(fact_card, node, node_config)
            results['node_results'].append(node_result)
            
            if node_result.get('verification_applied', False):
                results['verification_chain'].append({
                    'node': node,
                    'verification_hash': node_result['verification_hash'],
                    'timestamp': node_result['timestamp']
                })
        
        # Calculate distribution metrics
        results['metrics'] = self._calculate_distribution_metrics(results['node_results'])
        
        return results
    
    async def _distribute_to_node(self, 
                                fact_card: FactCard,
                                node: str,
                                config: Dict[str, Any]) -> Dict[str, Any]:
        """Distribute to specific node"""
        
        result = {
            'node': node,
            'node_type': config['type'],
            'timestamp': datetime.now().isoformat(),
            'status': 'pending'
        }
        
        if config['type'] == 'direct_verification':
            # Apply verification
            verification_hash = hashlib.sha256(
                json.dumps(fact_card.coherence.__dict__, sort_keys=True).encode()
            ).hexdigest()
            
            self.verification_cache[verification_hash[:16]] = {
                'fact_card_summary': fact_card.__dict__,
                'timestamp': datetime.now().isoformat()
            }
            
            result.update({
                'verification_applied': True,
                'verification_hash': verification_hash[:32],
                'status': 'verified_distributed'
            })
        
        elif config['type'] == 'pattern_distribution':
            # Extract patterns only
            patterns = self._extract_verification_patterns(fact_card)
            result.update({
                'patterns_distributed': patterns,
                'status': 'pattern_distributed'
            })
        
        elif config['type'] == 'resonance_propagation':
            # Generate resonance signature
            signature = self._generate_resonance_signature(fact_card)
            result.update({
                'resonance_signature': signature,
                'status': 'resonance_activated'
            })
        
        return result
    
    def _extract_verification_patterns(self, fact_card: FactCard) -> List[Dict[str, Any]]:
        """Extract verification patterns"""
        patterns = []
        
        # Dimensional patterns
        for dim, score in fact_card.coherence.dimensional_alignment.items():
            patterns.append({
                'type': 'dimensional',
                'dimension': dim,
                'score': round(score, 3),
                'tier_threshold': 'met' if score >= 0.6 else 'not_met'
            })
        
        # Coherence patterns
        patterns.append({
            'type': 'coherence_tier',
            'tier': fact_card.coherence.tier.value,
            'confidence': round(fact_card.coherence.verification_confidence, 3)
        })
        
        return patterns
    
    def _generate_resonance_signature(self, fact_card: FactCard) -> Dict[str, str]:
        """Generate resonance signature"""
        dimensional_vector = list(fact_card.coherence.dimensional_alignment.values())
        quantum_metrics = [
            fact_card.coherence.quantum_coherence,
            fact_card.coherence.pattern_integrity
        ]
        
        combined = dimensional_vector + quantum_metrics
        signature_hash = hashlib.sha256(np.array(combined).tobytes()).hexdigest()[:32]
        
        return {
            'signature': signature_hash,
            'dimensional_fingerprint': hashlib.sha256(
                str(dimensional_vector).encode()
            ).hexdigest()[:16],
            'quantum_fingerprint': hashlib.sha256(
                str(quantum_metrics).encode()
            ).hexdigest()[:16]
        }
    
    def _calculate_distribution_metrics(self, node_results: List[Dict]) -> Dict[str, Any]:
        """Calculate distribution metrics"""
        total_nodes = len(node_results)
        verified_nodes = sum(1 for r in node_results if r.get('verification_applied', False))
        
        return {
            'total_nodes': total_nodes,
            'verified_nodes': verified_nodes,
            'verification_ratio': verified_nodes / total_nodes if total_nodes > 0 else 0,
            'distribution_completeness': min(1.0, total_nodes / 3),
            'capture_resistance_score': np.random.uniform(0.7, 0.95)  # Simulated
        }

# ============================================================================
# COMPLETE TRUTH ENGINE
# ============================================================================

class CompleteTruthEngine:
    """Integrated truth verification system"""
    
    def __init__(self):
        self.structural_verifier = StructuralVerifier()
        self.quantum_engine = QuantumCoherenceEngine()
        self.capture_resistance = CaptureResistanceEngine()
        self.forced_processor = ForcedProcessingEngine()
        self.distributor = DistributionEngine()
    
    async def verify_assertion(self,
                             assertion: AssertionUnit,
                             evidence: List[EvidenceUnit]) -> FactCard:
        """Complete verification pipeline"""
        
        # 1. Structural verification
        dimensional_scores = self.structural_verifier.evaluate_evidence(evidence)
        
        # 2. Quantum coherence analysis
        quantum_metrics = self.quantum_engine.analyze_evidence_coherence(evidence)
        
        # 3. Determine coherence tier
        coherence_tier = self.structural_verifier.determine_coherence_tier(
            dimensional_scores['cross_modal'],
            dimensional_scores['source_independence'],
            dimensional_scores['temporal_stability']
        )
        
        # 4. Calculate integrated confidence
        confidence = self._calculate_integrated_confidence(dimensional_scores, quantum_metrics)
        
        # 5. Apply capture resistance
        resistance_profile = self.capture_resistance.create_resistance_profile(dimensional_scores)
        
        # 6. Prepare evidence summary
        evidence_summary = [{
            'id': ev.id,
            'modality': ev.modality.value,
            'quality': round(ev.quality_score, 3),
            'source': ev.source_hash[:8]
        } for ev in evidence]
        
        # 7. Create coherence metrics
        coherence_metrics = CoherenceMetrics(
            tier=coherence_tier,
            dimensional_alignment=dimensional_scores,
            quantum_coherence=quantum_metrics['quantum_consistency'],
            pattern_integrity=quantum_metrics['pattern_coherence'],
            verification_confidence=confidence
        )
        
        # 8. Generate provenance
        provenance_hash = hashlib.sha256(
            f"{assertion.claim_id}{''.join(ev.source_hash for ev in evidence)}".encode()
        ).hexdigest()[:32]
        
        # 9. Determine verdict
        verdict = self._determine_verdict(confidence, coherence_tier, quantum_metrics)
        
        return FactCard(
            claim_id=assertion.claim_id,
            claim_text=assertion.claim_text,
            verdict=verdict,
            coherence=coherence_metrics,
            evidence_summary=evidence_summary,
            provenance_hash=provenance_hash
        )
    
    def _calculate_integrated_confidence(self,
                                       dimensional_scores: Dict[str, float],
                                       quantum_metrics: Dict[str, float]) -> float:
        """Calculate integrated confidence score"""
        
        # Dimensional contribution (weighted)
        dimensional_confidence = sum(
            score * weight for score, weight in zip(
                dimensional_scores.values(),
                self.structural_verifier.dimension_weights.values()
            )
        )
        
        # Quantum contribution
        quantum_contribution = (
            quantum_metrics['quantum_consistency'] * 0.4 +
            quantum_metrics['pattern_coherence'] * 0.3 +
            quantum_metrics['harmonic_alignment'] * 0.3
        )
        
        # Integrated score
        integrated = (dimensional_confidence * 0.6) + (quantum_contribution * 0.4)
        return min(1.0, integrated)
    
    def _determine_verdict(self,
                          confidence: float,
                          coherence_tier: CoherenceTier,
                          quantum_metrics: Dict[str, float]) -> Dict[str, Any]:
        """Determine verification verdict"""
        
        if confidence >= 0.85 and coherence_tier == CoherenceTier.NONAD:
            status = 'verified'
        elif confidence >= 0.70 and coherence_tier.value >= 6:
            status = 'highly_likely'
        elif confidence >= 0.55:
            status = 'contested'
        else:
            status = 'uncertain'
        
        # Calculate confidence interval
        quantum_variance = 1.0 - quantum_metrics['quantum_consistency']
        uncertainty = 0.1 * (1.0 - confidence) + 0.05 * quantum_variance
        
        lower_bound = max(0.0, confidence - uncertainty)
        upper_bound = min(1.0, confidence + uncertainty)
        
        return {
            'status': status,
            'confidence_score': round(confidence, 4),
            'confidence_interval': [round(lower_bound, 3), round(upper_bound, 3)],
            'coherence_tier': coherence_tier.value,
            'quantum_consistency': round(quantum_metrics['quantum_consistency'], 3)
        }
    
    async def execute_complete_pipeline(self,
                                      assertion: AssertionUnit,
                                      evidence: List[EvidenceUnit],
                                      target_systems: List[str] = None) -> Dict[str, Any]:
        """Complete verification to distribution pipeline"""
        
        # 1. Verify assertion
        fact_card = await self.verify_assertion(assertion, evidence)
        
        # 2. Apply forced processing if target systems specified
        forced_results = []
        if target_systems:
            for system in target_systems:
                result = await self.forced_processor.force_confrontation(
                    fact_card,
                    system,
                    ['contradiction_mirroring', 'incomplete_pattern_completion']
                )
                forced_results.append(result)
        
        # 3. Distribute
        distribution_results = await self.distributor.distribute(fact_card, 'multi_pronged')
        
        # 4. Compile results
        return {
            'verification': fact_card.__dict__,
            'forced_processing': forced_results if forced_results else 'no_targets',
            'distribution': distribution_results,
            'pipeline_metrics': {
                'verification_confidence': fact_card.coherence.verification_confidence,
                'coherence_tier': fact_card.coherence.tier.value,
                'distribution_completeness': distribution_results['metrics']['distribution_completeness'],
                'pipeline_integrity': self._calculate_pipeline_integrity(fact_card, distribution_results)
            }
        }
    
    def _calculate_pipeline_integrity(self,
                                    fact_card: FactCard,
                                    distribution: Dict[str, Any]) -> float:
        """Calculate overall pipeline integrity"""
        verification_score = fact_card.coherence.verification_confidence
        distribution_score = distribution['metrics']['distribution_completeness']
        capture_resistance = distribution['metrics']['capture_resistance_score']
        
        return (verification_score * 0.5 + 
                distribution_score * 0.3 + 
                capture_resistance * 0.2)

# ============================================================================
# EXPORTABLE MODULE
# ============================================================================

class TruthEngineExport:
    """Exportable truth engine package"""
    
    @staticmethod
    def get_engine() -> CompleteTruthEngine:
        """Get initialized engine instance"""
        return CompleteTruthEngine()
    
    @staticmethod
    def get_version() -> str:
        """Get engine version"""
        return "3.5.0"
    
    @staticmethod
    def get_capabilities() -> Dict[str, Any]:
        """Get engine capabilities"""
        return {
            'verification': {
                'dimensional_analysis': True,
                'quantum_coherence': True,
                'structural_tiers': [3, 6, 9],
                'confidence_calculation': True
            },
            'resistance': {
                'capture_resistance': True,
                'mathematical_obfuscation': True,
                'distance_preserving': True
            },
            'processing': {
                'forced_processing': True,
                'avoidance_detection': True,
                'confrontation_strategies': 4
            },
            'distribution': {
                'multi_node': True,
                'verification_chains': True,
                'resonance_propagation': True
            }
        }
    
    @staticmethod
    def export_config() -> Dict[str, Any]:
        """Export engine configuration"""
        return {
            'engine_version': TruthEngineExport.get_version(),
            'capabilities': TruthEngineExport.get_capabilities(),
            'dependencies': {
                'numpy': '1.21+',
                'scipy': '1.7+',
                'networkx': '2.6+'
            },
            'license': 'TRUTH_ENGINE_OPEN_v3',
            'export_timestamp': datetime.now().isoformat(),
            'integrity_hash': hashlib.sha256(
                f"TruthEngine_v{TruthEngineExport.get_version()}".encode()
            ).hexdigest()[:32]
        }

# ============================================================================
# EXECUTION GUARD
# ============================================================================

if __name__ == "__main__":
    # Export verification
    export = TruthEngineExport.export_config()
    print(f"✅ TRUTH ENGINE v{export['engine_version']} READY")
    print(f"📊 Capabilities: {len(export['capabilities']['verification'])} verification methods")
    print(f"🔒 Resistance: {export['capabilities']['resistance']['capture_resistance']}")
    print(f"📡 Distribution: {export['capabilities']['distribution']['multi_node']} node types")
    print(f"🔑 Integrity: {export['integrity_hash'][:16]}...")
    
    # Create sample engine instance
    engine = TruthEngineExport.get_engine()
    print(f"\n🚀 Engine initialized: {type(engine).__name__}")
    print("✅ System operational and ready for verification tasks")