drop safety/availability from scoring, holistic p4 rating, phase-parallel pipeline

safety and availability don't reduce to physics formulas the way
power_density/range_fuel/cost_efficiency do -- they're judgment calls
(risk assessment, infrastructure prevalence), and running them through the
same log-normalize() built for physical quantities produced incoherent
results: safety's raw value is already a "0-1" score, and normalizing it
again turned 0.6 into an unexplainable 0.678 that even the LLM reviewing
it could only cite, never justify (see combo 1540). Removed both from
domain_metric_weights (safety from 4 domains, availability from
urban_commuting) and renormalized the remaining weights to sum to 1.0.

Pass 4 now produces one holistic RATING (LOW/MEDIUM/HIGH) alongside the
existing VERDICT, with safety and accessibility folded in as qualitative
considerations feeding that single judgment rather than scored
separately -- not a checklist of independent numbers. New
qualitative_rating column, filterable in the results UI. Also added
domain name/description to the review prompt so the LLM judges a metric
like range against what the domain actually needs (urban_commuting:
1-50km) instead of generic real-world expectations for the platform
category -- confirmed live on a combo where phi4 had called a 396km range
"limited" by comparing to typical aircraft rather than a domain that
needs 1-50km.

Pass 2 is estimator-only now -- self.llm is never consulted there,
reserved entirely for pass 4. Restructured Pipeline.run() from combo-first
to phase-parallel: each pass now runs to completion across every combo
before the next pass starts, rather than walking each combo through all
four passes before the next combo. This surfaced a real bug: domain-
blocked combos (status stays "valid" by design, not "_fail") were
slipping past a naive status-based skip guard and getting silently
re-processed by pass 2. Fixed with a shared dead-combo check that catches
both generic failures and domain blocks correctly.

Also fixes a results-page display bug found while reviewing a live combo:
the per-metric "position" bar showed raw distance from norm_min without
inverting for lower_is_better metrics, so an excellent cost score (near
the good end) rendered as a ~0%, near-empty bar -- looked bad next to its
own 0.99 normalized score.

Validated live against phi4 (real Ollama calls, not mocked): full-domain
phase-parallel run (2,970 combos, estimator-only p2, 1.6s) followed by a
real pass-4 run (111 reviewed, 11m, 0 crashes, 0 null ratings). Two tests
that relied on the old LLM-driven pass 2 to force deterministic outcomes
were updated to test pass 4's verdict-wiring directly instead. All 100
tests pass.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_pipeline_async.py

@@ -335,28 +335,34 @@ def test_p3_fail_below_threshold(seeded_repo):
 
 
 def test_p4_fail_implausible(seeded_repo):
-    """Combos deemed implausible by LLM should get p4_fail status."""
+    """Combos deemed implausible by LLM should get p4_fail status.
+
+    Pass 2 is estimator-only now (never calls the LLM), so there's no way
+    to force every combo's raw estimates toward a controlled low/high value
+    the way MockLLMProvider's default_estimates used to. Force the pass-4
+    verdict directly instead -- what's under test here is pipeline.py's
+    wiring of review_plausibility's return value to status/counters, not
+    MockLLMProvider's avg-based heuristic.
+    """
     from physcom.llm.providers.mock import MockLLMProvider
 
+    class AlwaysImplausibleLLM(MockLLMProvider):
+        def review_plausibility(self, description, raw_metrics, normalized_scores, domain):
+            return ("Always implausible for testing.", False)
+
     repo = seeded_repo
     domain = repo.get_domain("urban_commuting")
 
     resolver = ConstraintResolver()
     scorer = Scorer(domain)
-    # Low estimates → normalized scores avg <= 0.5 → MockLLMProvider returns (text, False)
     # Use threshold=0.0 so no combo gets p3_fail and all reach pass 4
-    mock_llm = MockLLMProvider(default_estimates={
-        "power_density": 0.1, "cost_efficiency": 0.1, "safety": 0.1,
-        "availability": 0.1, "range_fuel": 0.1,
-    })
-    pipeline = Pipeline(repo, resolver, scorer, llm=mock_llm)
+    pipeline = Pipeline(repo, resolver, scorer, llm=AlwaysImplausibleLLM())
 
     result = pipeline.run(
         domain, ["platform", "actuator", "energy_storage"],
         score_threshold=0.0, passes=[1, 2, 3, 4],
     )
 
-    # With low normalized scores (avg <= 0.5), reviewed combos should be p4_fail
     assert result.pass4_failed > 0
     assert result.pass4_reviewed == 0
 
@@ -367,20 +373,23 @@ def test_p4_fail_implausible(seeded_repo):
 
 
 def test_p4_pass_plausible(seeded_repo):
-    """Combos deemed plausible by LLM should get llm_reviewed status."""
+    """Combos deemed plausible by LLM should get llm_reviewed status.
+
+    See test_p4_fail_implausible on why the verdict is forced directly
+    rather than via controlled pass-2 estimates.
+    """
     from physcom.llm.providers.mock import MockLLMProvider
 
+    class AlwaysPlausibleLLM(MockLLMProvider):
+        def review_plausibility(self, description, raw_metrics, normalized_scores, domain):
+            return ("Always plausible for testing.", True)
+
     repo = seeded_repo
     domain = repo.get_domain("urban_commuting")
 
     resolver = ConstraintResolver()
     scorer = Scorer(domain)
-    # High estimates → avg > 0.5 → MockLLMProvider returns (text, True)
-    mock_llm = MockLLMProvider(default_estimates={
-        "power_density": 500.0, "cost_efficiency": 5e-4, "safety": 0.6,
-        "availability": 0.7, "range_fuel": 200000.0,
-    })
-    pipeline = Pipeline(repo, resolver, scorer, llm=mock_llm)
+    pipeline = Pipeline(repo, resolver, scorer, llm=AlwaysPlausibleLLM())
 
     result = pipeline.run(
         domain, ["platform", "actuator", "energy_storage"],