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结论句裁判与口径账本认分析结论：引用了分析结论的句子，裁判摘录里给方法与版本、判定语、判定标准、关键数、附带说明、结果表（被引用的行先给）、输入取自哪几个 Q 用了哪几列和它们的 SQL、下钻自哪个 A（核查 agent 的摘录照旧分段遮）；摘录里有分析结论时裁判规则另加一条（判定是系统算的，只判句子说法和判定是否一致，分析说明不了原因），没有分析结论的报告规则一字不差。口径账本不给分析结论里的数（判定语、具名标量、结果表的格）标口径、不按它们投影比对、不进整题判断的计数，也不算成没比对
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ThearchyHelios
+114
-3
@@ -44,6 +44,7 @@ from typing import Any
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from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
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from sqlalchemy import select
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from app.analysis.contract import CATALOG_PREFIX, LEDGER_KIND, catalog_result, is_result, method_label
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from app.core.artifact_store import canonical_json, content_hash
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from app.core.artifact_store import load as load_artifact
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from app.core.errors import describe_exception, not_configured
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@@ -160,6 +161,15 @@ JUDGE_RULES = f"""判定（verdict）五选一：
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7. used 写你实际用到的证据编号（摘录里【】中的那个，比如 m:gmv、Q1），没用到就写空数组。
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8. 每句都要给判定，unit 照抄句子前面方括号里的编号。"""
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#: 摘录里有分析结论（【A1】）时接在裁判规则后面的一条。只在这时才加：没有分析结论的报告，裁判看到的规则一字不差，
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#: 已判过的句子照旧复用（复用键里只有摘录，没有规则）
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ANALYSIS_JUDGE_PROMPT = (
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"9. 【A1】这类分析结论的判定、判定标准和关键数是系统按平台的方法算出来的，不要质疑也不要复算；只判断句子的说法和"
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"判定是否一致：「主要来自」「集中在」「超出常规波动」「呈上升趋势」「差异站得住」这类判断要和判定对得上，句子点名的段"
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"要在判定里，方向要一致；判定写着算不了的，不能支持任何判断。分析结论只说明变化来自哪里，说明不了原因：句子写了"
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"原因、证据里又没有原因的，判 partial 或 insufficient。"
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)
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#: 报告节点 claims 为 judge 时拼在写作规则后面：写作者要知道还有一道按证据的核对
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JUDGE_RULE = ("这份报告的结论句还会由另一个模型逐句核对：它只看你挂的依据，判断这句话站不站得住。"
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"说法不要超出证据——证据里没有的原因、范围、趋势，不要写成结论。")
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@@ -299,8 +309,8 @@ def _candidate(unit: dict[str, Any], priority: int, order: int, text: str, secti
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rows.append((alias, int(locator["row"])))
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if kind == "cell" and locator.get("column") not in (None, ""):
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cols.append((alias, str(locator["column"])))
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elif kind in ("query", "retrieval"):
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whole.append(alias)
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elif kind in ("query", "retrieval", LEDGER_KIND):
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whole.append(alias) # 分析结论：判定语、具名标量、依据 [[see:A1]] 都整份摘
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elif kind == "quote" and isinstance(locator.get("hit"), int):
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hits.append((alias, int(locator["hit"])))
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return Candidate(unit=str(unit.get("id")), priority=priority, order=order, text=text, section=section,
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@@ -664,6 +674,102 @@ def _retrieval_excerpt(alias: str, entry: dict[str, Any], picked: list[int], who
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return "\n".join(lines)
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def _analysis_alias(alias: Any) -> bool:
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"""写作目录里分析结论的编号（A1、A2……）。"""
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return isinstance(alias, str) and alias.startswith(CATALOG_PREFIX) and alias[len(CATALOG_PREFIX):].isdigit()
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#: 分析结论的摘录最多列几个具名的数
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ANALYSIS_VALUES = 16
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def _analysis_excerpt(alias: str, entry: dict[str, Any], rows: set[int], fetch: _Fetch,
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masked: dict[str, Any] | None, catalog: dict[str, Any], mask: Any = None) -> str | None:
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"""一条分析结论的摘录：方法、判定语、判定标准、关键数（具名标量）、附带说明、结果表（被引用的行先给，再从头补满
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EXCERPT_ROWS 行，最多 EXCERPT_COLS 列，表头写给人看的列名）、输入取自哪几次查询、用了哪几列，以及那几次查询的 SQL
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（截到 SQL_CHARS，最多 ENTITY_QUERIES 次）。判定是系统算的，裁判只判句子的说法和判定是否一致（ANALYSIS_JUDGE_PROMPT）。
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结论按工件取（经 fetch：按需裁判时只认封存范围内的工件，取不到、哈希不符的不给这段摘录）；目录条目带着整份结论的
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直接用。agent 写的假设不给：它不是结论。mask 同 _query_excerpt（核查 agent 的摘录才给）：SQL 先遮再截，结果表里的文字、
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判定语按值换，数照原样。"""
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result = catalog_result(entry)
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if result is None:
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content = fetch(entry.get("artifact"))
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result = content if is_result(content) else None
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if result is None:
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return None
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scrub = mask.text if mask is not None else (lambda t: t)
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lines = [f"【{alias}】分析结论（{method_label(result.get('op'), result.get('mode'))}，方法 {result.get('method') or '—'}）"]
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if result.get("status") == "refused":
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lines.append(f"判定：算不了（{_clean(str(result.get('reason') or '没有写原因'))}）")
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else:
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lines.append(f"判定：{scrub(_clean(str(result.get('phrase') or ''))) or '—'}")
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if criterion := _clean(str(result.get("criterion") or "")):
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lines.append(f"判定标准：{criterion}")
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values = result.get("values") if isinstance(result.get("values"), dict) else {}
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if values:
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shown = []
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for key, raw in list(values.items())[:ANALYSIS_VALUES]:
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try:
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text = render_cell(raw)
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except RenderError:
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text = MISSING
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shown.append(f"{key}={text if isinstance(raw, (int, float)) else scrub(text)}")
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more = f" | …另有 {len(values) - ANALYSIS_VALUES} 个" if len(values) > ANALYSIS_VALUES else ""
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lines.append("关键数：" + " | ".join(shown) + more)
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caveats = [_clean(str(c.get("text") or "")) for c in result.get("caveats") or [] if isinstance(c, dict)]
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if caveats := [c for c in caveats if c]:
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lines.append("附带说明：" + "；".join(scrub(c) for c in caveats))
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table = result.get("table") if isinstance(result.get("table"), dict) else None
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if table is not None and isinstance(table.get("rows"), list) and table["rows"]:
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columns = [str(c) for c in table.get("columns") or []][:EXCERPT_COLS]
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labels = table.get("labels") if isinstance(table.get("labels"), dict) else {}
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total = len(table["rows"])
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picked = sorted(r for r in rows if 0 <= r < total)[:EXCERPT_ROWS]
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picked += [r for r in range(total) if r not in picked][:EXCERPT_ROWS - len(picked)]
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lines.append(f"结果表（共 {total} 行）列：" + " | ".join(_clean(str(labels.get(c) or c)) for c in columns))
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for r in sorted(picked):
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cells = []
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for c in columns:
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try:
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raw, _ = locate_cell(table, r, c)
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text = render_cell(raw)
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cells.append(text if isinstance(raw, (int, float)) else scrub(text))
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except (CellError, RenderError):
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cells.append(MISSING)
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lines.append(f"r{r}：" + " | ".join(cells))
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sqls: list[str] = []
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seen: set[str] = set()
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for item in result.get("inputs") or []:
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if not isinstance(item, dict) or not item.get("artifact") or item.get("role") == "parent":
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continue
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artifact = str(item["artifact"])
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if artifact in seen:
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continue
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seen.add(artifact)
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query = _query_alias(catalog, artifact)
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snap = fetch(artifact)
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snap = snap if isinstance(snap, dict) else {}
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source = (catalog.get(query) or {}).get("source") if query else None
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hidden, names = _derived(snap, _source_hidden(source or snap.get("source"), catalog, fetch, masked)
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| _mask_names(snap.get("mask_columns")), fetch, mask)
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used = [str(c) for c in item.get("columns") or [] if str(c).lower() not in hidden]
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label = f"查询 {query}" if query else "一次查询（不在这份报告的证据目录中）"
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lines.append(f"输入：取自{label}" + (f"，用到 {'、'.join(used)} 列" if used else ""))
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sql = _clean(str(snap.get("sql") or ""))
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if sql and mask is not None:
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sql = mask.sql(sql, snap, names)
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if sql and len(sqls) < ENTITY_QUERIES:
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sqls.append(_sql_line(f"查询 {query} 的 SQL" if query else "这次查询的 SQL", sql))
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parents = [str(i.get("artifact")) for i in result.get("inputs") or []
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if isinstance(i, dict) and i.get("role") == "parent"]
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for artifact in parents:
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found = next((a for a, e in catalog.items() if isinstance(e, dict) and e.get("kind") == LEDGER_KIND
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and e.get("artifact") == artifact), None)
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lines.append(f"下钻自：{found or '一条分析结论（不在这份报告的证据目录中）'}")
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return "\n".join(lines + sqls)
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def _excerpt(alias: str, entry: dict[str, Any], cands: list[Candidate], fetch: _Fetch,
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masked: dict[str, Any] | None, catalog: dict[str, Any], *, provenance_on: bool = False,
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mask: Any = None) -> str | None:
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@@ -683,6 +789,9 @@ def _excerpt(alias: str, entry: dict[str, Any], cands: list[Candidate], fetch: _
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if kind == "retrieval":
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picked = [h for c in cands for a, h in c.hits if a == alias]
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return _retrieval_excerpt(alias, entry, picked, any(alias in c.whole for c in cands), fetch, mask)
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if kind == LEDGER_KIND:
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rows = {r for c in cands for a, r in c.rows if a == alias}
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return _analysis_excerpt(alias, entry, rows, fetch, masked, catalog, mask)
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if kind == "table":
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head = f"【{alias}】表 {entry.get('name')}" + (f"（数据源 {entry['source']}）" if entry.get("source") else "")
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return "\n".join([head, *_table_lines(entry, catalog, fetch, masked),
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@@ -754,8 +863,10 @@ class JudgeRequest:
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aliases = list(dict.fromkeys(a for c in units for a in c.aliases if a in self.excerpts))
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evidence = "\n\n".join(self.excerpts[a] for a in aliases) or "（这些句子都没有挂依据）"
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ordered = sorted(units, key=lambda c: c.order)
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# 摘录里有分析结论时另加一条（ANALYSIS_JUDGE_PROMPT）；没有的和以前一字不差
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rules = f"{JUDGE_RULES}\n{ANALYSIS_JUDGE_PROMPT}" if any(_analysis_alias(a) for a in aliases) else JUDGE_RULES
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return [
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SystemMessage(content=f"{JUDGE_ROLE}\n\n{JUDGE_RULES}"),
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SystemMessage(content=f"{JUDGE_ROLE}\n\n{rules}"),
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HumanMessage(content=(
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f"证据摘录（【】里是编号，判定时 used 写它）：\n{evidence}\n\n"
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"要判断的句子（每句都要给判定）：\n" + "\n".join(self.unit_line(c) for c in ordered) + "\n\n"
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@@ -20,6 +20,9 @@
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整题（whole_of）：主报告里所有带标签的数都来自同一口径的同一版、绑定一致，就记 equivalent，并写出还缺的入口字段（口径入口
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节点声明了、这次的绑定里没有的；required 且没有缺省值的另列）。
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分析结论里的数（[[a:A3]]、[[a:A3.键]]、[[v:A3.r0.列]]、整表 [[table:A3]] 展开的格）不标：它们是从查询快照派生出来的，
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不是口径（docs/design/analysis-round/00-总方案.md D11），不和口径比对、不进整题判断的计数，也不算成「没比对」。
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这里只是纯函数：标签在比对时算好、存进比对工件（ledger/match.py）；接口读的时候按这次运行查到过的遮罩值遮一遍（view_label）：
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绑定的值是遮罩值的不给值，差异条目里的 SQL 片段走证据面板同一套遮罩（api/evidence._Shown）。
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"""
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@@ -28,6 +31,8 @@ from __future__ import annotations
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from collections.abc import Callable, Mapping
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from typing import Any
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from app.analysis.contract import LEDGER_KIND
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#: 标签的四种、认法的两种
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KINDS = ("caliber", "related", "reasoning", "unknown")
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HOWS = ("independent", "composed")
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@@ -259,6 +264,8 @@ def referenced_columns(docs: list[Mapping[str, Any]],
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if cite.get("status") != "resolved":
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continue
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entry = catalog.get(cite.get("alias")) if isinstance(catalog.get(cite.get("alias")), dict) else {}
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if entry.get("kind") == LEDGER_KIND:
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continue # 分析结论的结果表不是查询，不比对
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locator = cite.get("locator") if isinstance(cite.get("locator"), dict) else {}
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artifact = str(entry.get("artifact") or "")
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if cite.get("kind") == "cell" and artifact and isinstance(locator.get("column"), str):
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@@ -375,7 +382,7 @@ def label_doc(doc: Mapping[str, Any], queries: Mapping[str, Mapping[str, Any]],
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calibers: Mapping[str, Mapping[str, Any]],
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load_card: Callable[[str], Mapping[str, Any] | None]) -> dict[str, dict[str, Any]]:
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"""一份报告文档里每个数的标签：{片段 id: 标签}。只标引用解析了的数字、取值片段（单元格、指标）；运行输入、表名、引文、
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没有引用的数不标。"""
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没有引用的数不标，分析结论里的数（具名标量、结果表的格）也不标（D11：派生的数，不是口径）。"""
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from app.engine.evidence import iter_units
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catalog = doc.get("catalog") if isinstance(doc.get("catalog"), dict) else {}
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@@ -389,6 +396,8 @@ def label_doc(doc: Mapping[str, Any], queries: Mapping[str, Mapping[str, Any]],
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if cite.get("status") != "resolved":
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continue
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entry = catalog.get(cite.get("alias")) if isinstance(catalog.get(cite.get("alias")), dict) else {}
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if entry.get("kind") == LEDGER_KIND:
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continue
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locator = cite.get("locator") if isinstance(cite.get("locator"), dict) else {}
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artifact = str(entry.get("artifact") or "")
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if cite.get("kind") == "cell" and artifact:
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