fix(understand): a network layer holds lines — valve, pump and annotation layers are no pipes

The field test ran the takeoffs on a real P&ID pair, kept outside the
repository. It showed that the default network layers took every layer the
vocabulary files under piping or electrical. That included the valve layers
(KLEPPEN, Valve_*), the pump layers (a "product pumps" layer reads as
product), and annotation layers such as pipe sizes, materials and valve
numbers. Symbol outlines and leader lines became pipe runs.

vocab.is_network_layer is now the one rule. A layer is a network layer when
it is piping or electrical at 0.9, its name names no equipment
(equipment_kind: valve, pump, tank, motor, panel, reducer), and it names no
annotation (sizes, materials, numbers, hatching, or an annotation word).
build_network's default layers, drawing_topology_check and the topo_*
focuses all use it, so they agree on which lines are the network.

Sixteen parametrized cases pin it, across symbol, annotation, pipe and cable
layers.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01FKFStjEFzpGyLq5bMpWxTd

engineering/understand/critique.py

@@ -34,12 +34,10 @@ from engineering.layers import PID_LAYERS
 from engineering.pid.lines import PID_LINE_LAYERS
 from engineering.plan_spec import Issue
 from engineering.understand.topology import (
-    NETWORK_CONFIDENCE,
-    NETWORK_DISCIPLINES,
     network_layers,
     topology_findings,
 )
-from engineering.understand.vocab import classify_layer
+from engineering.understand.vocab import is_network_layer
 
 if TYPE_CHECKING:
     from backends.base import AutoCADBackend
@@ -66,13 +64,7 @@ SEMANTIC_LAYERS: frozenset[str] = frozenset(
 
 
 def _is_candidate(name: str) -> bool:
-    if name in SEMANTIC_LAYERS:
-        return False
-    meaning = classify_layer(name)
-    return (
-        meaning.get("discipline") in NETWORK_DISCIPLINES
-        and float(meaning.get("confidence") or 0.0) >= NETWORK_CONFIDENCE
-    )
+    return name not in SEMANTIC_LAYERS and is_network_layer(name)
 
 
 def focus_layers(snap) -> list[str]: