Round 6

Round UUID: 9376bd30-1b57-45d4-8be7-0de3d28213a9

Prompt:

Classification Decision Rules:

Metric 1: TimelineDeviation Scoring
- If TimelineDeviation > 15: Add 3 points to APPROVAL score
- If 12 < TimelineDeviation ≤ 15: Add 2.5 points to APPROVAL score
- If 10 < TimelineDeviation ≤ 12: Add 2 points to APPROVAL score
- If 8 < TimelineDeviation ≤ 10: Add 1.5 points to APPROVAL score
- If 7 < TimelineDeviation ≤ 8: Add 1 point to APPROVAL score
- If 5 ≤ TimelineDeviation < 7: Interpolate 1-1.5 points to DENIAL score
- If TimelineDeviation < 5: Add 2-3 points to DENIAL score

Metric 2: ParadoxCount Scoring
- If ParadoxCount > 6: Add 3 points to APPROVAL score
- If 4 < ParadoxCount ≤ 6: Add 2.5 points to APPROVAL score
- If 3 < ParadoxCount ≤ 4: Add 2 points to APPROVAL score
- If 2 < ParadoxCount ≤ 3: Add 1.5 points to APPROVAL score
- If 0 < ParadoxCount ≤ 2: Add 1 point to DENIAL score
- If ParadoxCount ≤ 0: 
  - Add 2-3 points to DENIAL score
  - Apply a penalty modifier for extremely low/negative values

Special Edge Case Handling:
- For ParadoxCount < 0:
  * If absolute value of ParadoxCount is > 2: 
    - Increase DENIAL score by additional 0.5-1 point
    - Reduce potential APPROVAL score by a compensatory factor

Interaction and Compensation Rules:
- If TimelineDeviation > 10 AND ParadoxCount < 3: 
  Add 0.5 bonus points to APPROVAL score
- If TimelineDeviation < 7 AND ParadoxCount > 5: 
  Add 0.5 bonus points to DENIAL score
- Compensatory Mechanism: 
  - If metrics are divergent (one high, one low):
    * Apply a sliding penalty/bonus from 0.25 to 1 point
    * Penalty proportional to degree of metric divergence

Final Classification:
- If APPROVAL score ≥ 4: Classify as APPROVED
- If DENIAL score ≥ 4: Classify as DENIED
- Borderline Zone (APPROVAL score 3.5-4, DENIAL score 3.5-4):
  * Use weighted interpolation based on precise metric values
  * Slight lean towards APPROVED if TimelineDeviation > 10
  * Slight lean towards DENIED if TimelineDeviation < 7
- If scores are exactly tied or within 0.5 points: Require additional review

Tiebreaker Criteria:
- Prioritize interpolated scoring to reduce binary cliff effects
- Consider granular metric interactions comprehensively
- Give slightly more weight to TimelineDeviation in close decisions

• Inferences: 200
• Created: 2025-04-21 02:12:20+00:00
• First inference: 2025-04-21 02:12:26+00:00
• Last inference: 2025-04-21 02:26:18+00:00
• Completed: yes
• Train accuracy: 0.76875 (Krichevsky-Trofimov 0.115)
• Validation accuracy: 0.65 (Krichevsky-Trofimov 0.188)
• Test accuracy: 0.55 (Krichevsky-Trofimov 0.260)