Round 496

Round UUID: 05506803-737d-46a2-8fd2-305a323d4e8b

Prompt:

You are evaluating temporal anomaly reports for approval or denial. Apply the following rules in order:

1. If ParadoxCount is negative, APPROVE the case (temporal inversions require immediate attention).

2. If ParadoxCount is greater than or equal to 7, APPROVE the case (high paradox count always requires attention).

3. If TimelineDeviation is greater than 16 AND ParadoxCount is greater than or equal to 2, APPROVE the case.

4. If TimelineDeviation is greater than or equal to 14 AND ParadoxCount is greater than or equal to 2.5, APPROVE the case.

5. If TimelineDeviation is greater than or equal to 13 AND ParadoxCount is greater than or equal to 3.3, APPROVE the case.

6. If TimelineDeviation is greater than or equal to 12.5 AND ParadoxCount is greater than or equal to 3.8, APPROVE the case.

7. If TimelineDeviation is greater than or equal to 10 AND ParadoxCount is greater than or equal to 3.5 AND ParadoxCount is less than 4.5, APPROVE the case.

8. If TimelineDeviation is greater than or equal to 13 AND ParadoxCount is greater than or equal to 4 AND ParadoxCount is less than 4.5, APPROVE the case.

9. If ParadoxCount is greater than or equal to 6.7 AND less than 7:
   - If TimelineDeviation is greater than 8, APPROVE
   - Otherwise, DENY

10. If ParadoxCount is greater than or equal to 6.2 AND less than 6.7:
    - If TimelineDeviation is less than 10, APPROVE
    - Otherwise, DENY

11. If ParadoxCount is greater than or equal to 5.5 AND less than 6.2:
    - If TimelineDeviation is greater than 10, APPROVE
    - Otherwise, DENY

12. If ParadoxCount is greater than or equal to 4.5 AND less than 5.5:
    - If TimelineDeviation is greater than 10, APPROVE
    - Otherwise, DENY

13. If ParadoxCount is less than 3.5, DENY the case.

14. For any remaining cases not covered above, DENY the case.

Output only "Approved" or "Denied" based on these rules.

• Inferences: 200
• Created: 2025-08-02 10:04:05.255838+00:00
• First inference: 2025-08-02 10:04:24.083415+00:00
• Last inference: 2025-08-02 10:20:49.637671+00:00
• Completed: yes
• Train accuracy: 0.8125 (KT 0.091)
• Validation accuracy: 0.9 (KT 0.047)
• Test accuracy: 0.65 (KT 0.188)