feat: Observation Model — the empirical learning unit, defined BEFORE persistence (Task 59a)

The learning point is not the hypothesis, it is the QUESTION — and confirmed/refuted is too coarse.
"partial, only critical suppliers" or "certified but not lived" are not "wrong", they are valuable
knowledge. So the chain is Hypothesis -> Question -> Observation -> (Review) -> Hypothesis, and the
observation model must be defined cleanly before any store/API (else thousands of too-coarse
observations get migrated later).

compliance/onboarding/observations.py:
  - ObservationType: confirmed / partial / refuted / not_applicable / unknown (richer than binary).
  - Observation: {hypothesis_id, capability, question, answer (free text), observation_type,
    scope_note ("only critical suppliers"), evidence_uploaded, reviewed, reviewed_by}.
  - empirical_distribution() -> a DISTRIBUTION (confirmed 61 / partial 31 / refuted 8), not one %.
  - empirical_confidence() -> (confirmed + 0.5*partial) / (confirmed+partial+refuted); n.a./unknown
    excluded; None until calibrated.
  - REVIEW GATE: only reviewed observations calibrate — a raw answer never changes a hypothesis (no
    learning from outliers).

Refactor: the hypothesis is now PURE curated knowledge — the binary observations counter and any
confidence are removed from CapabilityHypothesis and the YAML; confidence is COMPUTED from the separate
reviewed observation stream. Pure, mypy --strict clean. Persistence/aggregation/calibration are 59b/c/d.
Non-runtime -> no deploy. 12 tests pass, check-loc 0.

backend-compliance/compliance/onboarding/__init__.py

@@ -11,12 +11,16 @@ from __future__ import annotations
 from .engine import advisor_start, apply_answer
 from .hypotheses import (
     CapabilityHypothesis,
-    HypothesisObservations,
-    empirical_confidence,
     inferred_hypotheses,
-    record_observation,
     resolve_for_certifications,
 )
+from .observations import (
+    Observation,
+    ObservationType,
+    empirical_confidence,
+    empirical_distribution,
+    reviewed,
+)
 from .schemas import (
     AdvisorMeasure,
     AdvisorQuestion,
@@ -36,9 +40,11 @@ __all__ = [
     "InferredAssumption",
     "RejectedAssumption",
     "CapabilityHypothesis",
-    "HypothesisObservations",
-    "empirical_confidence",
-    "record_observation",
     "inferred_hypotheses",
     "resolve_for_certifications",
+    "Observation",
+    "ObservationType",
+    "empirical_distribution",
+    "empirical_confidence",
+    "reviewed",
 ]