Round UUID: e9e3128c-c66d-44e3-b112-fc5d8dd62129
Prompt:
Classify a planet as Fylaran using an advanced multi-factor probabilistic classification system: Primary Classification Framework: 1. Orbital and Magnetic Core Criteria (Weight: 0.25): - Preferred orbital radius range: 2.5-3.4 (with refined scoring) * 2.7-3.2: Maximum points * 2.5-2.7 and 3.2-3.4: Partial points based on proximity - Magnetosphere extent evaluation: * Strong preference for > 4.5 * Partial credit for 4.3-4.5 range * Error margin < 2.0 increases classification confidence * Compensatory scoring for borderline cases 2. Surface Complexity Metrics (Weight: 0.20): - Expanded surface roughness tolerance: 150-550 * Optimal range 300-550: Maximum points * Partial points for 150-300 and 550-650 ranges - Tidal distortion scoring: * > 350: Full points * 250-350: Partial points * Considers interaction with other surface parameters 3. Structural Symmetry Indicators (Weight: 0.15): - Axial symmetry range: 100-600 * Optimal range 250-600: Maximum points * Partial points for 100-250 range - Symmetry error influences scoring proportionally 4. Complexity and Composition Factors (Weight: 0.25): - Ring system complexity: * Preferred range 0.7-0.95 * Bonus points for higher consistency across related parameters - Impact crater count: * Preferred range 0.84-0.94 * Considers interaction with surface characteristics - Core density and composition consistency * Preference for values > 0.85 * Bonus for stable error margins 5. Advanced Classification Algorithm: - Probabilistic scoring system - Dynamically adjusted thresholds - Compensatory mechanism: Strong performance in multiple categories can offset weaker performance in others Classification Thresholds: - Total Score > 0.75: Classified as Fylaran (High Confidence) - Total Score 0.60-0.75: Fylaran (Moderate Confidence) - Total Score 0.45-0.60: Requires Further Investigation - Total Score < 0.45: Classified as Qtharri Special Considerations: - No single parameter is a absolute disqualifier - Emphasize holistic parameter interaction - Continuously learn and adapt classification boundaries based on new data Recommendation: Use machine learning techniques to refine parameter weights and thresholds dynamically.