feat(F-16): chunk quality gate + section classification

- codex/quality.py: 3-signal filter (length / alpha-ratio / bib-score)
  + rule-based section classifier + run_quality_pass retroactive DB pass
- codex/ingest.py: promote quality imports to module level; apply
  filter_chunks before embedding; store section column in chunks INSERT
- codex/config.py: CHUNK_MIN_CHARS / CHUNK_MIN_ALPHA_RATIO / CHUNK_MAX_BIB_SCORE
- infra/schema.sql: ALTER TABLE chunks ADD COLUMN IF NOT EXISTS section TEXT
- .env.example: document F-16 quality thresholds
- codex/cli.py: quality run sub-command (scope by --paper-id or all papers)
- tests/quality/: 35 new tests covering all quality functions
- tests/ingest/: patch filter_chunks in source-path tests to isolate ingest

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -27,3 +27,8 @@ OPENALEX_MAILTO=you@example.com
 # Nougat HTTP-Server (Jetson: http://192.168.178.103:8080 | lokal: http://localhost:8080)
 # Used by `codex ingest <id> --rich` for full-PDF Mathpix Markdown output.
 NOUGAT_URL=http://localhost:8080
+
+# F-16 Chunk Quality Gate thresholds (all optional — defaults shown)
+CHUNK_MIN_CHARS=60          # Discard chunks shorter than this many characters
+CHUNK_MIN_ALPHA_RATIO=0.40  # Discard chunks with < 40% alphabetic characters
+CHUNK_MAX_BIB_SCORE=0.70    # Discard chunks scoring above this bibliography threshold

codex/cli.py

@@ -13,6 +13,7 @@ discover_app = typer.Typer(help="Discovery queries over the citation graph.")
 prov_app = typer.Typer(help="Provenance: @cite scan, code_links, bib export.")
 wiki_app = typer.Typer(help="Wiki-compile: grounded concept pages over the RAG substrate.")
 synth_app = typer.Typer(help="Synthesis: grounded leads and quarantined conjectures (F-13).")
+quality_app = typer.Typer(help="Chunk quality gate and section classification (F-16).")
 # F-09: search sub-app with paper (semantic) and formula (FTS) subcommands
 search_app = typer.Typer(
     help="Search: semantic paper search and formula full-text search.",
@@ -22,6 +23,7 @@ app.add_typer(discover_app, name="discover")
 app.add_typer(prov_app, name="provenance")
 app.add_typer(wiki_app, name="wiki")
 app.add_typer(synth_app, name="synthesis")
+app.add_typer(quality_app, name="quality")
 app.add_typer(search_app, name="search")
 
 
@@ -419,9 +421,7 @@ def synthesis_conjectures(
     conjectures = propose_conjectures(top_k=settings.synthesis_top_k, llm=llm)
     write_leads(conjectures, leads_dir)
 
-    typer.echo(
+    typer.echo(f"Conjectures (UNVERIFIED) written to {leads_dir}/conjectures/: {len(conjectures)}")
-        f"Conjectures (UNVERIFIED) written to {leads_dir}/conjectures/: {len(conjectures)}"
-    )
 
 
 @synth_app.command("report")
@@ -454,13 +454,45 @@ def synthesis_report(
 
     typer.echo("")
     typer.echo("=" * 72)
-    typer.echo(
+    typer.echo(f"CONJECTURES  ({len(conj_files)})  → {conj_dir}  [UNVERIFIED — never in wiki/]")
-        f"CONJECTURES  ({len(conj_files)})  → {conj_dir}  "
-        "[UNVERIFIED — never in wiki/]"
-    )
     typer.echo("=" * 72)
     if not conj_files:
         typer.echo("(none)")
     for f in conj_files:
         first = f.read_text(encoding="utf-8").splitlines()[0]
         typer.echo(f"  {first}")
+
+
+# ---------------------------------------------------------------------------
+# F-16 — quality sub-commands
+# ---------------------------------------------------------------------------
+
+
+@quality_app.command("run")
+def quality_run(
+    paper_id: Optional[str] = typer.Option(  # noqa: UP045
+        None,
+        "--paper-id",
+        help="Restrict pass to one paper (omit to process all papers).",
+    ),
+) -> None:
+    """Apply quality gate + section classification to existing chunks.
+
+    Iterates over chunks in the database, removes those that fail the
+    quality thresholds, and back-fills the ``section`` column for the rest.
+    """
+    from codex.config import get_settings
+    from codex.db import get_conn
+    from codex.quality import run_quality_pass
+
+    settings = get_settings()
+    with get_conn() as conn:
+        stats = run_quality_pass(paper_id=paper_id, conn=conn, settings=settings)
+
+    scope = f"paper {paper_id}" if paper_id else "all papers"
+    typer.echo(
+        f"Quality pass ({scope}): "
+        f"{stats['kept']} kept, "
+        f"{stats['removed']} removed, "
+        f"{stats['tagged']} section-tagged"
+    )

codex/config.py

@@ -191,9 +191,7 @@ class Settings(BaseSettings):
 
     synthesis_llm_url: str | None = Field(
         default=None,
-        description=(
+        description=("Ollama base URL for synthesis. When None, falls back to OLLAMA_BASE_URL."),
-            "Ollama base URL for synthesis. When None, falls back to OLLAMA_BASE_URL."
-        ),
     )
 
     synthesis_top_k: int = Field(
@@ -256,6 +254,38 @@ class Settings(BaseSettings):
         ),
     )
 
+    # ------------------------------------------------------------------
+    # F-16 Chunk Quality Gate
+    # ------------------------------------------------------------------
+    chunk_min_chars: int = Field(
+        default=60,
+        gt=0,
+        description=(
+            "Minimum character count for a chunk to pass the quality gate. "
+            "Chunks shorter than this value are discarded before embedding."
+        ),
+    )
+
+    chunk_min_alpha_ratio: float = Field(
+        default=0.40,
+        ge=0.0,
+        le=1.0,
+        description=(
+            "Minimum fraction of alphabetic characters in a chunk. "
+            "Chunks with a lower ratio are treated as OCR artefacts and discarded."
+        ),
+    )
+
+    chunk_max_bib_score: float = Field(
+        default=0.70,
+        ge=0.0,
+        le=1.0,
+        description=(
+            "Maximum bibliography heuristic score before a chunk is rejected. "
+            "Score is based on DOI density, 'et al.' and year-in-brackets patterns."
+        ),
+    )
+
 
 @lru_cache(maxsize=1)
 def get_settings() -> Settings:

codex/ingest.py

@@ -13,6 +13,7 @@ from codex.config import get_settings
 from codex.db import get_conn
 from codex.embed import get_embedder
 from codex.models import Citation, Paper
+from codex.quality import classify_section, filter_chunks
 from codex.sources import openalex, semanticscholar
 
 logger = logging.getLogger(__name__)
@@ -80,7 +81,6 @@ def ingest_paper(
     # ---------------------------------------------------------------
     paper: Paper | None = openalex.fetch_paper(paper_id)
 
-
     if paper is None:
         # Detect arXiv IDs: numeric pattern like "2301.07041" or "arxiv:..." prefix
         pid_lower = paper_id.lower()
@@ -177,7 +177,8 @@ def ingest_paper(
             else:
                 logger.warning("Unbekannter Dateityp: %s — kein Text-Parsing", source_path)
 
-            chunks_text = chunk_text(text) if text else []
+            raw_chunks = chunk_text(text) if text else []
+            chunks_text = filter_chunks(raw_chunks, settings=get_settings())
 
             # Delete existing chunks for this paper
             conn.execute("DELETE FROM chunks WHERE paper_id = %s", (paper.id,))
@@ -186,13 +187,19 @@ def ingest_paper(
                 # Embed all chunks in one batch
                 chunk_embeddings = embedder.encode_dense(chunks_text)
                 chunk_rows = [
-                    (paper.id, ord_idx, content, chunk_embeddings[ord_idx].tolist())
+                    (
+                        paper.id,
+                        ord_idx,
+                        content,
+                        chunk_embeddings[ord_idx].tolist(),
+                        classify_section(content),
+                    )
                     for ord_idx, content in enumerate(chunks_text)
                 ]
                 with conn.cursor() as cur:
                     cur.executemany(
-                        "INSERT INTO chunks (paper_id, ord, content, embedding)"
+                        "INSERT INTO chunks (paper_id, ord, content, embedding, section)"
-                        " VALUES (%s, %s, %s, %s)",
+                        " VALUES (%s, %s, %s, %s, %s)",
                         chunk_rows,
                     )
                 chunks_upserted = len(chunk_rows)
@@ -207,8 +214,7 @@ def ingest_paper(
             # OpenAlex returns citing_id=openalex_id, but papers.id uses the DOI.
             # Rewrite citing_id to paper.id so the FK constraint is satisfied.
             api_citations = [
-                Citation(citing_id=paper.id, cited_id=c.cited_id, context=c.context)
+                Citation(citing_id=paper.id, cited_id=c.cited_id, context=c.context) for c in raw
-                for c in raw
             ]
         else:
             api_citations = semanticscholar.fetch_references(paper_id)

codex/quality.py

@@ -0,0 +1,189 @@
+"""Chunk quality filtering and section classification (F-16).
+
+Three independent quality signals are applied at ingest time:
+
+1. **Length** — structural; no content required.
+2. **Alpha-ratio** — OCR artefacts have high non-alpha character density.
+3. **Bib-score** — DOI + "et al." + (YYYY) patterns co-occur almost only
+   in reference list entries.
+
+Section classification is rule-based (no LLM) and runs on the first 200
+characters of each chunk.  The retroactive ``run_quality_pass`` function
+can be called from the CLI to back-fill existing chunks.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from codex.config import Settings
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Section classification patterns (checked in order; first match wins)
+# ---------------------------------------------------------------------------
+
+_SECTION_PATTERNS: list[tuple[str, re.Pattern[str]]] = [
+    ("abstract", re.compile(r"^\s*(abstract|zusammenfassung|r[eé]sum[eé])\b", re.I)),
+    ("intro", re.compile(r"^\s*(introduction|einleitung|1\.\s)", re.I)),
+    ("theorem", re.compile(r"^\s*(theorem|lemma|proposition|corollary|definition)\b", re.I)),
+    ("proof", re.compile(r"^\s*(proof\b|beweis\b|proof\s+of\b)", re.I)),
+    ("bibliography", re.compile(r"^\s*(references|bibliography|bibliographie|literatur)\b", re.I)),
+]
+
+_DOI_RE = re.compile(r"10\.\d{4,}/\S+")
+_ET_AL_RE = re.compile(r"\bet\s+al\b", re.I)
+_YEAR_BRACKETS_RE = re.compile(r"\(\d{4}\)")
+
+
+# ---------------------------------------------------------------------------
+# Bibliography heuristic score
+# ---------------------------------------------------------------------------
+
+
+def _bib_score(text: str) -> float:
+    """Return a [0..1] heuristic for how bibliography-like a chunk is.
+
+    Three signals contribute:
+    * DOI occurrences (weight 3)
+    * "et al." occurrences (weight 2)
+    * year-in-brackets occurrences (weight 1)
+
+    The raw signal is normalised against a rough word-count proxy so that
+    long chunks with occasional references don't get flagged.
+    """
+    if not text:
+        return 0.0
+    word_count = max(len(text.split()), 1)
+    doi_hits = len(_DOI_RE.findall(text))
+    etal_hits = len(_ET_AL_RE.findall(text))
+    year_hits = len(_YEAR_BRACKETS_RE.findall(text))
+    raw = (doi_hits * 3 + etal_hits * 2 + year_hits) / max(word_count / 10, 1)
+    return min(raw, 1.0)
+
+
+# ---------------------------------------------------------------------------
+# Quality predicate
+# ---------------------------------------------------------------------------
+
+
+def is_quality_chunk(text: str, *, settings: Settings) -> bool:
+    """Return True when *text* passes all three quality thresholds.
+
+    Checks (all configurable via :class:`codex.config.Settings`):
+
+    1. ``chunk_min_chars`` — character count floor.
+    2. ``chunk_min_alpha_ratio`` — minimum fraction of alphabetic chars.
+    3. ``chunk_max_bib_score`` — bibliography heuristic ceiling.
+    """
+    if len(text) < settings.chunk_min_chars:
+        return False
+    alpha_ratio = sum(c.isalpha() for c in text) / max(len(text), 1)
+    if alpha_ratio < settings.chunk_min_alpha_ratio:
+        return False
+    return _bib_score(text) <= settings.chunk_max_bib_score
+
+
+# ---------------------------------------------------------------------------
+# Section classification
+# ---------------------------------------------------------------------------
+
+
+def classify_section(text: str) -> str:
+    """Classify a chunk's section using rule-based regex matching.
+
+    Inspects only the first 200 characters.  Returns one of:
+    ``abstract``, ``intro``, ``theorem``, ``proof``, ``bibliography``, ``body``.
+    """
+    snippet = text[:200]
+    for section_name, pattern in _SECTION_PATTERNS:
+        if pattern.search(snippet):
+            return section_name
+    # Fallback: bibliography by DOI density even without a header
+    if _bib_score(text) > 0.5:
+        return "bibliography"
+    return "body"
+
+
+# ---------------------------------------------------------------------------
+# Batch filter
+# ---------------------------------------------------------------------------
+
+
+def filter_chunks(chunks: list[str], *, settings: Settings) -> list[str]:
+    """Return only the chunks that pass all quality filters.
+
+    Logs the keep ratio at DEBUG level.
+    """
+    kept = [c for c in chunks if is_quality_chunk(c, settings=settings)]
+    logger.debug(
+        "Quality filter: %d/%d chunks kept (%.0f%%)",
+        len(kept),
+        len(chunks),
+        100 * len(kept) / max(len(chunks), 1),
+    )
+    return kept
+
+
+# ---------------------------------------------------------------------------
+# Retroactive DB pass
+# ---------------------------------------------------------------------------
+
+
+def run_quality_pass(
+    *,
+    paper_id: str | None = None,
+    conn: Any,
+    settings: Settings,
+) -> dict[str, int]:
+    """Apply quality filter + section classification to existing chunks in DB.
+
+    For each chunk:
+    * Fails quality → DELETE.
+    * Passes quality → UPDATE ``section`` with :func:`classify_section`.
+
+    Parameters
+    ----------
+    paper_id:
+        When provided, restrict the pass to chunks for this paper only.
+    conn:
+        Open psycopg connection (dict-row factory assumed).
+    settings:
+        Application settings for quality thresholds.
+
+    Returns
+    -------
+    dict with keys ``kept``, ``removed``, ``tagged`` (= kept).
+    """
+    if paper_id is not None:
+        rows = conn.execute(
+            "SELECT id, content FROM chunks WHERE paper_id = %(pid)s",
+            {"pid": paper_id},
+        ).fetchall()
+    else:
+        rows = conn.execute("SELECT id, content FROM chunks").fetchall()
+
+    kept = 0
+    removed = 0
+    for row in rows:
+        chunk_id = row["id"]
+        content = row["content"]
+        if not is_quality_chunk(content, settings=settings):
+            conn.execute("DELETE FROM chunks WHERE id = %(id)s", {"id": chunk_id})
+            removed += 1
+        else:
+            section = classify_section(content)
+            conn.execute(
+                "UPDATE chunks SET section = %(section)s WHERE id = %(id)s",
+                {"section": section, "id": chunk_id},
+            )
+            kept += 1
+
+    conn.commit()
+    logger.info("Quality pass done: %d kept, %d removed, %d section-tagged", kept, removed, kept)
+    return {"kept": kept, "removed": removed, "tagged": kept}

infra/schema.sql

@@ -122,3 +122,6 @@ CREATE TABLE IF NOT EXISTS figures (
 CREATE INDEX IF NOT EXISTS figures_emb_idx
     ON figures USING hnsw (embedding vector_cosine_ops);
 CREATE INDEX IF NOT EXISTS figures_paper_idx ON figures (paper_id);
+
+-- F-16 Chunk Quality Gate: section classification
+ALTER TABLE chunks ADD COLUMN IF NOT EXISTS section TEXT;

tests/ingest/test_ingest.py

@@ -131,6 +131,7 @@ def test_ingest_paper_source_tex(tmp_path: Any) -> None:
         patch("codex.ingest.get_conn", side_effect=_make_conn_cm(mock_conn)),
         patch("codex.parsing.tex.latex_to_text", return_value="Hello world. Intro text."),
         patch("codex.parsing.tex.chunk_text", return_value=fake_chunks),
+        patch("codex.ingest.filter_chunks", side_effect=lambda chunks, settings: chunks),
     ):
         result = ingest_paper(paper.id, source_path=str(tex_file))
 
@@ -178,6 +179,7 @@ def test_ingest_paper_source_pdf(tmp_path: Any) -> None:
         patch("codex.parsing.nougat.pdf_to_markdown", return_value="pdf markdown text."),
         patch("codex.parsing.tex.chunk_text", return_value=fake_chunks),
         patch("codex.parsing.grobid.extract_references", return_value=grobid_refs),
+        patch("codex.ingest.filter_chunks", side_effect=lambda chunks, settings: chunks),
     ):
         result = ingest_paper(paper.id, source_path=str(pdf_file))
 

tests/quality/test_quality.py

@@ -0,0 +1,245 @@
+"""Tests for codex.quality — chunk quality gate and section classification (F-16)."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+from codex.quality import (
+    _bib_score,
+    classify_section,
+    filter_chunks,
+    is_quality_chunk,
+    run_quality_pass,
+)
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _settings(min_chars: int = 60, min_alpha: float = 0.40, max_bib: float = 0.70):
+    s = MagicMock()
+    s.chunk_min_chars = min_chars
+    s.chunk_min_alpha_ratio = min_alpha
+    s.chunk_max_bib_score = max_bib
+    return s
+
+
+GOOD_TEXT = (
+    "In differential geometry, the discrete Laplace–Beltrami operator "
+    "generalises the smooth operator to polyhedral surfaces by using cotangent "
+    "weights. This discretisation preserves key spectral properties."
+)
+
+BIB_TEXT = (
+    "Bobenko, A. and Pinkall, U. (2015) Discrete differential geometry. "
+    "doi:10.1145/2010324.1964978 Smith et al. (2018) Surface remeshing. "
+    "doi:10.1007/s00211-019-01082-w Jones et al. (2020) doi:10.1145/1234567.890123"
+)
+
+
+# ---------------------------------------------------------------------------
+# _bib_score
+# ---------------------------------------------------------------------------
+
+
+class TestBibScore:
+    def test_empty_string(self):
+        assert _bib_score("") == 0.0
+
+    def test_clean_prose_is_low(self):
+        assert _bib_score(GOOD_TEXT) < 0.10
+
+    def test_bib_like_text_is_high(self):
+        assert _bib_score(BIB_TEXT) > 0.70
+
+    def test_single_doi_short_text_spikes(self):
+        text = "See doi:10.1145/2010324.1964978 for details."
+        # 8 words, 1 DOI (weight 3) → raw = 3 / max(0.8, 1) = 3.0 → capped 1.0
+        assert _bib_score(text) == 1.0
+
+    def test_score_capped_at_one(self):
+        # _DOI_RE requires \d{4,} — use a valid registrant length
+        heavy = "10.1145/abcdef " * 20
+        assert _bib_score(heavy) == 1.0
+
+
+# ---------------------------------------------------------------------------
+# is_quality_chunk
+# ---------------------------------------------------------------------------
+
+
+class TestIsQualityChunk:
+    def test_passes_good_text(self):
+        assert is_quality_chunk(GOOD_TEXT, settings=_settings()) is True
+
+    def test_fails_too_short(self):
+        assert is_quality_chunk("Short.", settings=_settings(min_chars=60)) is False
+
+    def test_fails_low_alpha_ratio(self):
+        # Mostly digits and symbols
+        junk = "1234 5678 9012 3456 7890 |||| ==== ---- ____ .... ;;;; 0000 9999 8888 7777"
+        assert is_quality_chunk(junk, settings=_settings(min_alpha=0.40)) is False
+
+    def test_fails_high_bib_score(self):
+        assert is_quality_chunk(BIB_TEXT, settings=_settings(max_bib=0.70)) is False
+
+    def test_bib_threshold_is_strict_gt(self):
+        # A text that scores exactly at the threshold should still pass (> not >=)
+        s = _settings(max_bib=1.0)
+        assert is_quality_chunk(GOOD_TEXT, settings=s) is True
+
+    def test_exactly_min_chars_passes(self):
+        # check is `len < min_chars`, so exactly at the threshold passes
+        text = "a" * 60
+        assert is_quality_chunk(text, settings=_settings(min_chars=60)) is True
+
+    def test_one_below_min_chars_fails(self):
+        text = "a" * 59
+        assert is_quality_chunk(text, settings=_settings(min_chars=60)) is False
+
+
+# ---------------------------------------------------------------------------
+# classify_section
+# ---------------------------------------------------------------------------
+
+
+class TestClassifySection:
+    def test_abstract_header(self):
+        assert classify_section("Abstract\n\nThis paper presents...") == "abstract"
+
+    def test_abstract_german(self):
+        assert classify_section("Zusammenfassung\nDiese Arbeit...") == "abstract"
+
+    def test_intro_header(self):
+        assert classify_section("Introduction\nOver the past decade...") == "intro"
+
+    def test_intro_numbered(self):
+        assert classify_section("1. Introduction\nWe study...") == "intro"
+
+    def test_theorem_header(self):
+        assert classify_section("Theorem 3.1 (Existence). Let M be...") == "theorem"
+
+    def test_lemma_header(self):
+        assert classify_section("Lemma 2. If f is smooth then...") == "theorem"
+
+    def test_proof_header(self):
+        assert classify_section("Proof. We proceed by induction...") == "proof"
+
+    def test_bibliography_header(self):
+        assert classify_section("References\n[1] Bobenko, A....") == "bibliography"
+
+    def test_bibliography_german(self):
+        assert classify_section("Literatur\n[1] Müller, H....") == "bibliography"
+
+    def test_body_fallback(self):
+        assert classify_section("The cotangent weight for edge (i,j) is...") == "body"
+
+    def test_doi_density_fallback_to_bibliography(self):
+        # No "References" header but heavy DOI density → bibliography
+        dense = BIB_TEXT
+        assert classify_section(dense) == "bibliography"
+
+    def test_only_first_200_chars_checked(self):
+        # Pattern in position >200 should NOT be matched as section header
+        prefix = "x" * 201
+        text = prefix + "\nAbstract"
+        assert classify_section(text) == "body"
+
+
+# ---------------------------------------------------------------------------
+# filter_chunks
+# ---------------------------------------------------------------------------
+
+
+class TestFilterChunks:
+    def test_empty_list(self):
+        assert filter_chunks([], settings=_settings()) == []
+
+    def test_all_good(self):
+        chunks = [GOOD_TEXT, GOOD_TEXT + " More content here."]
+        assert filter_chunks(chunks, settings=_settings()) == chunks
+
+    def test_all_bad(self):
+        chunks = ["x", "y"]
+        assert filter_chunks(chunks, settings=_settings()) == []
+
+    def test_mixed(self):
+        chunks = [GOOD_TEXT, "Short.", GOOD_TEXT]
+        result = filter_chunks(chunks, settings=_settings())
+        assert len(result) == 2
+        assert all(c == GOOD_TEXT for c in result)
+
+
+# ---------------------------------------------------------------------------
+# run_quality_pass
+# ---------------------------------------------------------------------------
+
+
+def _make_mock_conn(rows: list[dict]) -> MagicMock:
+    """Return a mock psycopg connection whose execute().fetchall() returns rows."""
+    conn = MagicMock()
+    conn.execute.return_value.fetchall.return_value = rows
+    return conn
+
+
+class TestRunQualityPass:
+    def test_removes_bad_chunk(self):
+        conn = _make_mock_conn([{"id": 1, "content": "x"}])
+        stats = run_quality_pass(conn=conn, settings=_settings())
+        assert stats["removed"] == 1
+        assert stats["kept"] == 0
+        # The DELETE must have been called with chunk id=1
+        delete_calls = [c for c in conn.execute.call_args_list if "DELETE" in str(c)]
+        assert len(delete_calls) == 1
+
+    def test_tags_good_chunk(self):
+        conn = _make_mock_conn([{"id": 2, "content": GOOD_TEXT}])
+        stats = run_quality_pass(conn=conn, settings=_settings())
+        assert stats["kept"] == 1
+        assert stats["tagged"] == 1
+        assert stats["removed"] == 0
+        # UPDATE must have been called
+        update_calls = [c for c in conn.execute.call_args_list if "UPDATE" in str(c)]
+        assert len(update_calls) == 1
+
+    def test_mixed_batch(self):
+        rows = [
+            {"id": 1, "content": "x"},
+            {"id": 2, "content": GOOD_TEXT},
+            {"id": 3, "content": "y"},
+            {"id": 4, "content": GOOD_TEXT + " extra words here."},
+        ]
+        conn = _make_mock_conn(rows)
+        stats = run_quality_pass(conn=conn, settings=_settings())
+        assert stats["removed"] == 2
+        assert stats["kept"] == 2
+        assert stats["tagged"] == 2
+
+    def test_paper_id_scopes_query(self):
+        conn = _make_mock_conn([])
+        run_quality_pass(paper_id="2301.07041", conn=conn, settings=_settings())
+        first_call = conn.execute.call_args_list[0]
+        sql = first_call[0][0]
+        assert "paper_id" in sql
+
+    def test_all_papers_query_has_no_filter(self):
+        conn = _make_mock_conn([])
+        run_quality_pass(paper_id=None, conn=conn, settings=_settings())
+        first_call = conn.execute.call_args_list[0]
+        sql = first_call[0][0]
+        assert "paper_id" not in sql
+
+    def test_commit_is_called(self):
+        conn = _make_mock_conn([])
+        run_quality_pass(conn=conn, settings=_settings())
+        conn.commit.assert_called_once()
+
+    def test_section_label_stored_correctly(self):
+        conn = _make_mock_conn([{"id": 5, "content": "Abstract\n\n" + GOOD_TEXT}])
+        run_quality_pass(conn=conn, settings=_settings())
+        update_calls = [c for c in conn.execute.call_args_list if "UPDATE" in str(c)]
+        assert len(update_calls) == 1
+        kwargs = update_calls[0][0][1]
+        assert kwargs["section"] == "abstract"
+        assert kwargs["id"] == 5