feat(sense): T-G3 标签+校准（labeler 四规则推导 + split-conformal + last-good）

- labeler.py：derive_true_tier 纯函数（规则0 人工优先 / plan_multi->T3 /
  失败->下一档(T3保持) / 成功->executed / 空结果跳过）+ derive_true_tiers
  批量回填 + 180d 留存清理
- calibrate.py：compute_thresholds（τ1/τ3 分组扫描——并列分数整组判定，
  精度 >= 1-α 的最大覆盖阈值；<min_labels -> ok=False 沿用 last-good）；
  save_thresholds 工件落盘+登记；load_active 回退链 active->同kind扫描->last-good->保守值
- store：all_observations/list_artifacts 支撑方法
- 测试 +9（四规则/批量清理/覆盖达标/标签不足/工件往返与回退），全量 395 passed

gateway/sense/calibrate.py

@@ -0,0 +1,132 @@
+"""split-conformal 阈值校准（T-G3）+ last-good 回退 + 工件读写（D-G3）。
+
+目标（§5 policy.alpha）：P(true>T1 | 判 T1) ≤ α（τ3 同理对 T3）。
+实现：calib 集按分数降序扫描，取**满足精度的最低阈值**（覆盖率最大）；
+无任何满足点 -> 阈值取最高分 + ε（全拒，保守）。
+标签数 < min_labels -> 沿用 last-good（D-G3）-> 再无 -> 内置保守值。
+"""
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from gateway.sense.store import json_dumps
+
+CONSERVATIVE = {"t1": 0.90, "t3": 0.90}
+THRESHOLD_KIND = "thresholds"
+
+
+def _threshold_for(samples: List[Tuple[float, bool]], alpha: float) -> Optional[float]:
+    """样本 [(score, true==tier)] -> 满足精度 >= 1-α 的最低阈值（并列分数整组判定）。
+
+    按唯一分数降序累积计数；每组边界处检查精度；取满足精度的最大覆盖（最低边界）。
+    无任何满足点 -> None（调用方全拒，保守）。
+    """
+    if not samples:
+        return None
+    groups: Dict[float, List[int]] = {}
+    for score, ok in samples:
+        g = groups.setdefault(score, [0, 0])
+        g[0] += 1
+        g[1] += 1 if ok else 0
+    total = 0
+    correct = 0
+    best: Optional[float] = None
+    for score in sorted(groups, reverse=True):
+        cnt, ok_cnt = groups[score]
+        total += cnt
+        correct += ok_cnt
+        if correct / total >= 1.0 - alpha:
+            best = score
+    return best
+
+
+def compute_thresholds(rows: List[Dict[str, Any]], alpha: float = 0.05,
+                       min_labels: int = 500) -> Dict[str, Any]:
+    """rows: [{probs(JSON 串或 dict), true_tier}] -> {t1, t3, coverage, n, ok}。
+
+    ok=False 表示标签不足（沿用 last-good 的信号）。
+    coverage = 通过阈值的 calib 样本占比（实测覆盖率，§6 晋升门比对 α+2%）。
+    """
+    labeled = []
+    for r in rows:
+        probs = r.get("probs")
+        if isinstance(probs, str):
+            try:
+                probs = json.loads(probs)
+            except json.JSONDecodeError:
+                continue
+        if not isinstance(probs, dict):
+            continue
+        true_tier = r.get("true_tier")
+        if true_tier not in ("T1", "T2", "T3"):
+            continue
+        labeled.append((float(probs.get("t1", 0)), float(probs.get("t3", 0)),
+                        true_tier))
+    n = len(labeled)
+    if n < min_labels:
+        return {"t1": None, "t3": None, "coverage": 0.0, "n": n, "ok": False}
+
+    s1 = [(p1, t == "T1") for p1, _p3, t in labeled]
+    s3 = [(p3, t == "T3") for _p1, p3, t in labeled]
+    t1 = _threshold_for(s1, alpha)
+    t3 = _threshold_for(s3, alpha)
+    if t1 is None or t3 is None:
+        return {"t1": t1, "t3": t3, "coverage": 0.0, "n": n, "ok": False}
+    coverage = (sum(1 for p1, _p3, _t in labeled if p1 >= t1)
+                + sum(1 for _p1, p3, _t in labeled if p3 >= t3)) / (2 * n)
+    return {"t1": round(t1, 4), "t3": round(t3, 4),
+            "coverage": round(coverage, 4), "n": n, "ok": True}
+
+
+def save_thresholds(store, models_dir: str | Path, version: str,
+                    th: Dict[str, Any], activate: bool = False) -> str:
+    """阈值工件落盘 thresholds.json + 登记工件表。返回文件路径。"""
+    d = Path(models_dir) / version
+    d.mkdir(parents=True, exist_ok=True)
+    path = d / "thresholds.json"
+    path.write_text(json_dumps(th), encoding="utf-8")
+    store.register_artifact(version, THRESHOLD_KIND, str(path),
+                            {k: th.get(k) for k in ("t1", "t3", "coverage", "n")},
+                            active=activate)
+    return str(path)
+
+
+def load_active(store, models_dir: str | Path) -> Dict[str, Any]:
+    """active 阈值工件 -> 旧工件扫描回退（last-good）-> 内置保守值（D-G3 回退链）。"""
+    art = store.active_artifact(THRESHOLD_KIND)
+    if art:
+        th = _read_threshold(art)
+        if th is not None:
+            th.setdefault("version", art["version"])
+            return th
+        # active 文件缺失/损坏：按时间倒序扫描同 kind 工件（last-good 语义）
+        for cand in store.list_artifacts(THRESHOLD_KIND):
+            if cand["version"] == art["version"]:
+                continue
+            th = _read_threshold(cand)
+            if th is not None:
+                th.setdefault("version", cand["version"])
+                return th
+    last_good = Path(models_dir) / "last-good.json"
+    if last_good.exists():
+        try:
+            th = json.loads(last_good.read_text(encoding="utf-8"))
+            if th.get("t1") is not None and th.get("t3") is not None:
+                th.setdefault("version", "last-good")
+                return th
+        except (json.JSONDecodeError, OSError):
+            pass
+    return {**CONSERVATIVE, "version": "conservative"}
+
+
+def _read_threshold(art: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    try:
+        th = json.loads(Path(art["path"]).read_text(encoding="utf-8"))
+        if isinstance(th, dict) and th.get("t1") is not None and th.get("t3") is not None:
+            return th
+    except (json.JSONDecodeError, OSError):
+        pass
+    return None