ML Learning Foundation
Benchmark learning supports review prioritization and reference comparison. It does not train a scientific-inference model or produce scientific conclusions.
Training disabled. Autonomous inference disabled. Adaptive optimization deferred. External model APIs disabled. Human validation required.
Learning Policy
Foundation: enabled
Benchmark scoring: enabled
Training: disabled
Autonomous inference: disabled
Adaptive optimization: disabled
Scientific claims: disabled
External model APIs: disabled
Adaptive Learning Roadmap
1. Review-priority scoring (current)
2. Human validation (current)
3. Supervised learning (future)
4. Active learning (future)
5. Contextual bandit (future)
6. Advanced adaptive optimization (future)
Review Feature Snapshots
Feature snapshots capture operational metadata only. Source records are never mutated.
Baseline Scorer
Heuristic review priority scoring (0-100).
Does not score life, anomaly, biosignature, or habitability.
Adaptive Optimization Deferred
Advanced adaptive optimization is deferred until sufficient validated outcomes, labels, and review feedback exist.
No autonomous update occurs. No model is retrained. No review feedback triggers automated behavior.
Scientific Caution
No ML output means anomaly, biosignature, habitability, atmosphere, or life detection.
Baseline scores are operational prioritization only.
All ML outputs require human validation.
No external model service is enabled.
Learning Signals
All learning records are operational only and require human review before use.