Round 62

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.