docs: pin metrics compute layer designation

backend/src/edcmbone/metrics/compute.py

@@ -3,6 +3,19 @@
 ~~~~~~~~~~~~~~~~~~~~~~~~
 Computes the EDCM metric vector M_t and dissonance energy for a round.
 
+Layer designation
+-----------------
+Decision: A — behavioral/orchestration layer, not Operator L0.
+
+This module is retained temporarily inside edcmbone for compatibility, but
+canonically belongs upstream in the future `edcm` package. It consumes parsed
+round text, token statistics, marker phrases, and prior-round comparisons. It
+may carry `bone_count` through its result container for audit continuity, but it
+does not compute the L0 Operator vector and must not be treated as the
+bones-only Operator substrate.
+
+The L0 Operator entry point remains separate: `core/operator/operator_extractor.py`.
+
 The metric vector M_t ∈ ℝ^11 covers:
   C  Constraint strain     [0,1]
   R  Refusal density       [0,1]
@@ -47,6 +60,20 @@
 from .risk import fixation_risk, loop_risk
 
 
+# ---------------------------------------------------------------------------
+# Layer contract
+# ---------------------------------------------------------------------------
+
+LAYER_DECISION = "A_BEHAVIORAL_ORCHESTRATION"
+LAYER_DESIGNATION = "L1_L2_L3_ORCHESTRATOR_PENDING_EDCM_MIGRATION"
+LAYER_INPUT_SUBSTRATE = "round_text_tokens_marker_stats_prior_round_context"
+OPERATOR_LAYER_SUBSTRATE = "bones_only"
+OPERATES_ON_OPERATOR_BONES = False
+CONSUMES_BONE_COUNT_FOR_AUDIT = True
+MIGRATION_TARGET = "edcm"
+OPERATOR_ENTRYPOINT = "core.operator.operator_extractor"
+
+
 # ---------------------------------------------------------------------------
 # Result container
 # ---------------------------------------------------------------------------
@@ -257,7 +284,8 @@ def _compute_E(round_text, tokens_b, tokens_a, canon):
 # Public compute function
 # ---------------------------------------------------------------------------
 
-def compute_round(round_, prev_round=None, canon=None,
+def compute_round(round_, prev_round=None,
+                  canon=None,
                   alpha=0.85, delta_max=0.3,
                   prev_kappa=0.0, prev_entropy=0.0):
     """Compute the metric vector for a Round.
@@ -346,4 +374,4 @@ def compute_transcript(parsed_transcript, canon=None, alpha=0.85, delta_max=0.3)
         prev_kappa = m.kappa
         prev_entropy = shannon_entropy(tokenize(" ".join(t.text for t in rnd.turns)))
 
-    return results
+    return results

tests/test_metrics_layer_designation.py

@@ -0,0 +1,72 @@
+import importlib.util
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+BACKEND_SRC = ROOT / "backend" / "src"
+
+
+def _load_package(package_name: str, package_dir: Path):
+    existing = sys.modules.get(package_name)
+    if existing is not None:
+        return existing
+
+    spec = importlib.util.spec_from_file_location(
+        package_name,
+        package_dir / "__init__.py",
+        submodule_search_locations=[str(package_dir)],
+    )
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[package_name] = module
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+def _load_module(module_name: str, module_path: Path):
+    existing = sys.modules.get(module_name)
+    if existing is not None:
+        return existing
+
+    spec = importlib.util.spec_from_file_location(module_name, module_path)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+_load_package("edcmbone", BACKEND_SRC / "edcmbone")
+_load_package("edcmbone.metrics", BACKEND_SRC / "edcmbone" / "metrics")
+compute = _load_module(
+    "edcmbone.metrics.compute",
+    BACKEND_SRC / "edcmbone" / "metrics" / "compute.py",
+)
+
+_load_package("core", ROOT / "core")
+_load_package("core.operator", ROOT / "core" / "operator")
+operator_extractor = _load_module(
+    "core.operator.operator_extractor",
+    ROOT / "core" / "operator" / "operator_extractor.py",
+)
+
+
+def test_metrics_compute_is_behavioral_orchestration_not_operator_l0():
+    assert compute.LAYER_DECISION == "A_BEHAVIORAL_ORCHESTRATION"
+    assert compute.OPERATES_ON_OPERATOR_BONES is False
+    assert compute.CONSUMES_BONE_COUNT_FOR_AUDIT is True
+    assert compute.OPERATOR_LAYER_SUBSTRATE == "bones_only"
+    assert compute.MIGRATION_TARGET == "edcm"
+
+
+def test_operator_entrypoint_remains_separate_from_metrics_compute():
+    assert compute.OPERATOR_ENTRYPOINT == "core.operator.operator_extractor"
+    assert hasattr(operator_extractor, "compute_operator_for_turn")
+    assert hasattr(operator_extractor, "compute_per_turn_operator")
+    assert hasattr(operator_extractor, "compute_operator_windows")
+
+
+def test_metrics_compute_exposes_upper_layer_vector_shape():
+    sample = compute.RoundMetrics(C=1, R=2, F=3, E=4, D=5, N=6, I=7, O=8, L=9, P=10, kappa=11)
+    assert sample.vector() == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+    assert "bone_count" in sample.as_dict()