close guardrail gaps and fix the scoring pipeline top to bottom

Constraint resolver: aggregate mass/footprint across a combo instead of
pairwise-only checks, treat medium/atmosphere as agreement not supply/demand,
reduce multi-provider checks by best/sum instead of AND-ing every provider,
fail closed on unrecognized mutex values, add a propulsion-viability
(thrust-to-weight) rule. Seed data updated to match (nuclear/solar-sail
footprint floors, water-medium exclusions, explicit ground/gravity providers).

Domain metric units were stored globally per metric name instead of
per-domain, silently corrupting cost_efficiency for every domain but the
first one seeded — fixed with a schema migration.

Stub estimator's cost_efficiency/safety/availability/reliability were a
backwards formula and flat constants; replaced with heuristics grounded in
each entity's thrust_profile/energy_form/infrastructure.

LLM estimate_physics() now receives each metric's unit and expected range
instead of a bare name, fixing wildly miscalibrated estimates traced back to
the prompt's own hardcoded example anchoring the model to the wrong order of
magnitude. Sharpened the safety-estimation and plausibility-review prompts.
Deduped provider parsing logic into llm/parsing.py.

Web pipeline form can now pick an LLM provider per run instead of only via
server env var.

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

src/physcom/llm/parsing.py

@@ -0,0 +1,30 @@
+"""Shared response-parsing helpers for LLM providers."""
+
+from __future__ import annotations
+
+import json
+import re
+
+from physcom.models.domain import MetricBound
+
+
+def parse_verdict(text: str) -> bool:
+    """Extract VERDICT: PLAUSIBLE/IMPLAUSIBLE from response; default to True."""
+    m = re.search(r"VERDICT:\s*(PLAUSIBLE|IMPLAUSIBLE)", text, re.IGNORECASE)
+    if m:
+        return m.group(1).upper() == "PLAUSIBLE"
+    return True
+
+
+def parse_metric_json(text: str, metrics: list[MetricBound]) -> dict[str, float]:
+    """Strip markdown fences and parse JSON; fall back to each metric's own
+    norm_min/norm_max midpoint on error — a flat constant like 0.5 is
+    guaranteed wrong-magnitude for at least some metrics regardless of unit.
+    """
+    names = {mb.metric_name for mb in metrics}
+    text = re.sub(r"```(?:json)?\s*", "", text).strip().rstrip("`").strip()
+    try:
+        data = json.loads(text)
+        return {k: float(v) for k, v in data.items() if k in names}
+    except (json.JSONDecodeError, ValueError, TypeError):
+        return {mb.metric_name: (mb.norm_min + mb.norm_max) / 2 for mb in metrics}