Round UUID: ba4b97f5-26fe-4ed6-b4a5-e87a89948191
Prompt:
Classification Rules for Entity Effectiveness:
1. Adaptive Intensity-Shift Interaction Model:
a) Intensity Compensation Mechanism:
- For FizzIntensity < 40, apply an exponential compensation factor:
* ColourShift 15-20: Boost effectiveness probability by 0.3
* ColourShift 20-25: Boost effectiveness probability by 0.5
* ColourShift > 25: Boost effectiveness probability by 0.7
- Special Low-Intensity Rule:
* If FizzIntensity < 30 AND ColourShift > 15:
- Automatically elevate base effectiveness score
- Trigger enhanced review process
b) Dynamic Interaction Scoring:
- Calculate Non-Linear Interaction Score:
* Score = (FizzIntensity^1.2 * 0.004) + (ColourShift^1.1 * 0.02)
- Interaction Multipliers:
* If Score > 2.5: Multiply effectiveness by 1.5
* If Score 1.5-2.5: Multiply effectiveness by 1.2
* If Score 0.5-1.5: Multiply effectiveness by 1.0
* If Score < 0.5: Multiply effectiveness by 0.7
2. Refined Effectiveness Thresholds:
- Final Adjusted Score > 0.75: Classify as Effective
- Final Adjusted Score 0.45-0.75: Conduct Detailed Probabilistic Review
- Final Adjusted Score < 0.45: Classify as Ineffective
3. Boundary Case Management:
- Precision Adjustment Rules:
* High ColourShift (>22) with Moderate FizzIntensity (40-50):
- Apply additional verification step
- Reduce false positive probability
* Low FizzIntensity (<35) with Moderate ColourShift (10-18):
- Implement conservative classification approach
- Require higher ColourShift to confirm effectiveness
4. Anomaly and Edge Case Handling:
- Extreme Scenarios:
* ColourShift > 25 requires mandatory comprehensive review
* Negative or zero ColourShift automatically triggers ineffective classification
- Uncertainty Protocol:
* For borderline cases, prioritize contextual signal detection
* Use probabilistic classification with confidence intervals
Classification Decision Process:
- Implement multi-factor, non-linear scoring mechanism
- Dynamically adjust thresholds based on complex variable interactions
- Minimize classification errors through adaptive, context-aware approach
Key Improvement Focus:
- More nuanced handling of low-intensity scenarios
- Sophisticated interaction modeling between FizzIntensity and ColourShift
- Reduced false positive and false negative rates through intelligent thresholding