N1: ledger reconciliation (gate pass / true call count, backfilled block, outage, collisions, logs, throughput)

Items: FIRC0001-EDIT-02 EDIT-03 EDIT-18 ADVE-03 ADVE-05 ADVE-07 AI_C-01 AI_C-07 DATA-02 DATA-03 DATA-14
ECON-11 ECON-12 THEO-02 THEO-05 THEO-16 EDIT-05 ADVE-06 AI_C-08 DATA-04 ECON-06 LITE-07 THEO-10 DATA-08 ADVE-12 EDIT-15.
Read-only DuckDB on M3U96b (12 s). 703,725 of 1,123,098 ledger rows pass the gate (true calls);
backfilled block 154,964 = 153,894 + 1,070 rows < 30 words; 0 ledger rows inside the 5.25 h outage gap;
304 sections / 412 collided mentions; 14 silent JSON-decode failures outside the outage plus one
anomalous post-resume batch (2,686 calls, 23 productive vs 536 expected); 149,352 reconciled from the log.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 23 h ago (Nov 5, 2026) parent 7680606 Browse files

22 changed files +3,078 −0

added code/audit/a01_ledger_reconciliation.py +374 −0

		`@@ -0,0 +1,374 @@`
	1	+"""N1 - ledger reconciliation (clusters C, G, J-throughput, H-log numbers). Read-only.
	2	+
	3	+(a) _PROJECT_GATE over every ledger section (DuckDB RE2, case-insensitive) -> gate pass and the
	4	+ true call count by form x item x run; productive rate / mentions / USD per call.
	5	+(b) backfilled block (rows at min(extracted_at)), word_count < 30 rows, outage-window rows,
	6	+ zero-mention share per flush timestamp and per 5-minute bin.
	7	+(c) sections where ledger n_mentions > stored mentions (id collisions).
	8	+(d) run-log parser (October data/logs/spid_extract.log, June project-db/logs/extract.log).
	9	+(e) wall-clock / outage / effective throughput from ledger timestamps.
	10	+(f) backend values, confidence = 0.600 count, amount_raw formatting rule, min confidence by run.
	11	+"""
	12	+from __future__ import annotations
	13	+import json, re, sys, datetime as dt
	14	+from pathlib import Path
	15	+from collections import Counter, defaultdict
	16	+sys.path.insert(0, str(Path(__file__).resolve().parent))
	17	+from common import *
	18	+
	19	+GATE = (r"\b(project\|initiative\|program\|invest\|expan\|construct\|build\|launch\|develop\|"
	20	+ r"deploy\|implement\|acqui\|partner\|facilit\|capacity\|capital\|migrat\|transform\|"
	21	+ r"pipeline\|plant\|data ?cent\|automat\|sustainab\|renewable\|restructur)\w*")
	22	+OUTAGE_START = "2026-10-28 01:10:00" # window named in the consolidated review (THEO-05 / DATA-02)
	23	+OUTAGE_END = "2026-10-28 06:22:00"
	24	+PAPER_USD_TOTAL = 775.0 # construct.tex:35 'roughly 775 USD'
	25	+PAPER_USD_PER_SECTION = 0.0008 # construct.tex:35
	26	+JUNE_LOG_SECTIONS = 153894 # project-db/logs/extract.log FULL EXTRACT COMPLETE
	27	+JUNE_LOG_MENTIONS = 39930 # TOTAL mentions in DB after the June run
	28	+
	29	+r = Run("ledger")
	30	+con = connect(SPID_DB)
	31	+
	32	+# ---- (a) section metadata + gate flag for every section visible to the pipeline --------------
	33	+print("building sec_meta (regex over the parquet text, streamed) ...", flush=True)
	34	+con.execute(f"""
	35	+CREATE TEMP TABLE sec_meta AS
	36	+SELECT section_id, accession_number, section_name, word_count, length(text) AS nchar,
	37	+ regexp_matches(text, '{GATE}', 'i') AS gate, 'parsed' AS segmenter
	38	+FROM src_sections
	39	+UNION ALL
	40	+SELECT section_id, accession_number, section_name, word_count, length(text) AS nchar,
	41	+ regexp_matches(text, '{GATE}', 'i') AS gate, 'legacy_ingest' AS segmenter
	42	+FROM spid_sections
	43	+""")
	44	+r.notes["sec_meta_rows"] = con.execute("select count(*) from sec_meta").fetchone()[0]
	45	+
	46	+con.execute("""
	47	+CREATE TEMP TABLE stored AS
	48	+SELECT section_id, count(*) AS stored_mentions,
	49	+ list(project_type \|\| ':' \|\| left(project_name, 40)) AS stored_keys
	50	+FROM project_mentions GROUP BY 1
	51	+""")
	52	+tmin = con.execute("select min(extracted_at) from extracted_sections").fetchone()[0]
	53	+r.notes["min_extracted_at"] = str(tmin)
	54	+con.execute(f"""
	55	+CREATE TEMP TABLE ledger AS
	56	+SELECT e.section_id, e.accession_number, e.backend, e.n_mentions, e.extracted_at,
	57	+ CASE WHEN e.extracted_at = TIMESTAMP '{tmin}' THEN 'backfilled_june' ELSE 'october' END AS run,
	58	+ m.section_name, m.word_count, m.nchar, m.gate, m.segmenter,
	59	+ f.form_type, f.filing_date, f.cik, f.fiscal_year,
	60	+ co.in_sp500, co.in_russell3000,
	61	+ coalesce(s.stored_mentions, 0) AS stored_mentions, s.stored_keys
	62	+FROM extracted_sections e
	63	+LEFT JOIN sec_meta m USING (section_id)
	64	+LEFT JOIN src_filings f ON f.accession_number = e.accession_number
	65	+LEFT JOIN src_companies co ON f.cik = co.cik
	66	+LEFT JOIN stored s USING (section_id)
	67	+""")
	68	+n_ledger = con.execute("select count(*) from ledger").fetchone()[0]
	69	+n_unmatched = con.execute("select count(*) from ledger where section_name is null").fetchone()[0]
	70	+r.notes["ledger_rows"] = n_ledger
	71	+r.notes["ledger_rows_without_section_metadata"] = n_unmatched
	72	+
	73	+agg = """
	74	+ count(*) AS ledger_rows,
	75	+ count(*) FILTER (WHERE word_count < 30) AS lt30_words,
	76	+ count(*) FILTER (WHERE gate) AS gate_pass,
	77	+ count(*) FILTER (WHERE gate AND word_count >= 30) AS calls,
	78	+ count(*) FILTER (WHERE NOT gate AND n_mentions = 0) AS gate_rejected_zero,
	79	+ count(*) FILTER (WHERE NOT gate AND n_mentions > 0) AS gate_fail_but_mentions,
	80	+ count(*) FILTER (WHERE n_mentions > 0) AS with_mentions,
	81	+ sum(n_mentions) AS ledger_mentions,
	82	+ sum(stored_mentions) AS stored_mentions,
	83	+ count(DISTINCT cik) AS issuers,
	84	+ round(100.0 * count() FILTER (WHERE n_mentions > 0) / count(), 2) AS productive_pct_ledger,
	85	+ round(100.0 * count() FILTER (WHERE n_mentions > 0) / nullif(count() FILTER (WHERE gate AND word_count >= 30),0), 2) AS productive_pct_calls,
	86	+ round(sum(n_mentions) * 1.0 / count(*), 4) AS mentions_per_ledger_row,
	87	+ round(sum(n_mentions) * 1.0 / nullif(count(*) FILTER (WHERE gate AND word_count >= 30),0), 4) AS mentions_per_call
	88	+"""
	89	+r.dump("gate_pass_by_form_item.csv", con, f"SELECT form_type, section_name AS item, run, {agg} FROM ledger GROUP BY 1,2,3 ORDER BY 1,2,3")
	90	+r.dump("gate_pass_by_form.csv", con, f"SELECT form_type, {agg} FROM ledger GROUP BY 1 ORDER BY 1")
	91	+r.dump("gate_pass_by_run.csv", con, f"SELECT run, {agg} FROM ledger GROUP BY 1 ORDER BY 1")
	92	+r.dump("gate_pass_by_form_run.csv", con, f"SELECT form_type, run, {agg} FROM ledger GROUP BY 1,2 ORDER BY 1,2")
	93	+tot = con.execute(f"SELECT {agg} FROM ledger").fetchone()
	94	+cols = ["ledger_rows","lt30_words","gate_pass","calls","gate_rejected_zero","gate_fail_but_mentions","with_mentions","ledger_mentions","stored_mentions","issuers","productive_pct_ledger","productive_pct_calls","mentions_per_ledger_row","mentions_per_call"]
	95	+T = dict(zip(cols, tot))
	96	+oct_calls = con.execute("select count(*) filter (where gate and word_count>=30) from ledger where run='october'").fetchone()[0]
	97	+oct_rows = con.execute("select count(*) from ledger where run='october'").fetchone()[0]
	98	+T["usd_per_call_paper_total_775"] = round(PAPER_USD_TOTAL / oct_calls, 6)
	99	+T["usd_per_october_row_paper_total_775"] = round(PAPER_USD_TOTAL / oct_rows, 6)
	100	+T["usd_per_call_from_0_0008_x_ledger"] = round(PAPER_USD_PER_SECTION * n_ledger / T["calls"], 6)
	101	+T["october_rows"] = oct_rows; T["october_calls"] = oct_calls
	102	+T["headline_before"] = {"sections_submitted": 1123098, "productive_pct": 15.0, "mentions_per_section": 0.27, "usd_per_section": 0.0008}
	103	+r.write_json("gate_pass_totals.json", T)
	104	+
	105	+# ---- (b) backfilled block, < 30 words, outage window, flush timestamps -----------------------
	106	+B = {}
	107	+B["min_extracted_at"] = str(tmin)
	108	+B["rows_at_min_timestamp"] = con.execute("select count(*) from ledger where run='backfilled_june'").fetchone()[0]
	109	+B["june_log_sections"] = JUNE_LOG_SECTIONS
	110	+B["gap_rows_minus_log"] = B["rows_at_min_timestamp"] - JUNE_LOG_SECTIONS
	111	+B["block_ledger_mentions"] = con.execute("select sum(n_mentions) from ledger where run='backfilled_june'").fetchone()[0]
	112	+B["block_stored_mentions"] = con.execute("select sum(stored_mentions) from ledger where run='backfilled_june'").fetchone()[0]
	113	+B["june_log_mentions"] = JUNE_LOG_MENTIONS
	114	+B["block_lt30_words"] = con.execute("select count(*) from ledger where run='backfilled_june' and word_count<30").fetchone()[0]
	115	+B["block_gate_fail"] = con.execute("select count(*) from ledger where run='backfilled_june' and not gate").fetchone()[0]
	116	+B["block_lt30_or_gate_fail"] = con.execute("select count(*) from ledger where run='backfilled_june' and (word_count<30 or not gate)").fetchone()[0]
	117	+B["block_ge30_and_gate"] = con.execute("select count(*) from ledger where run='backfilled_june' and word_count>=30 and gate").fetchone()[0]
	118	+B["october_lt30_words"] = con.execute("select count(*) from ledger where run='october' and word_count<30").fetchone()[0]
	119	+B["block_by_form"] = con.execute("select form_type, count(), sum(n_mentions), count() filter (where word_count<30) from ledger where run='backfilled_june' group by 1 order by 1").fetchall()
	120	+B["block_by_segmenter"] = con.execute("select segmenter, count(*) from ledger where run='backfilled_june' group by 1").fetchall()
	121	+B["block_sp500_share"] = con.execute("select count() filter (where in_sp500), count() from ledger where run='backfilled_june'").fetchone()
	122	+B["block_ciks"] = con.execute("select count(distinct cik) from ledger where run='backfilled_june'").fetchone()[0]
	123	+B["outage_window"] = [OUTAGE_START, OUTAGE_END]
	124	+B["rows_in_outage_window"] = con.execute(f"select count(*), sum(n_mentions) from ledger where extracted_at between TIMESTAMP '{OUTAGE_START}' and TIMESTAMP '{OUTAGE_END}'").fetchone()
	125	+B["rows_0110_0640"] = con.execute("select count(*), sum(n_mentions) from ledger where extracted_at between TIMESTAMP '2026-10-28 01:10:00' and TIMESTAMP '2026-10-28 06:40:53'").fetchone()
	126	+B["rows_by_hour_1028"] = con.execute("select hour(extracted_at) h, count(), count() filter (where n_mentions=0) zeros, sum(n_mentions) from ledger where run='october' and extracted_at::date = DATE '2026-10-28' group by 1 order by 1").fetchall()
	127	+B["rows_by_hour_1027"] = con.execute("select hour(extracted_at) h, count(), count() filter (where n_mentions=0) zeros, sum(n_mentions) from ledger where run='october' and extracted_at::date = DATE '2026-10-27' group by 1 order by 1").fetchall()
	128	+B["paper_outage_rows_149352"] = 149352
	129	+r.write_json("backfill_block.json", B)
	130	+
	131	+r.dump("ledger_by_5min.csv", con, """
	132	+SELECT time_bucket(INTERVAL '5 minutes', extracted_at) AS bin5, count() AS rows, count() FILTER (WHERE n_mentions>0) AS with_mentions,
	133	+ sum(n_mentions) AS mentions, round(100.0count() FILTER (WHERE n_mentions=0)/count(*),2) AS zero_pct,
	134	+ count(*) FILTER (WHERE gate AND word_count>=30) AS calls, count(DISTINCT extracted_at) AS flushes
	135	+FROM ledger WHERE run='october' GROUP BY 1 ORDER BY 1""")
	136	+r.dump("ledger_by_minute.csv", con, """
	137	+SELECT time_bucket(INTERVAL '1 minute', extracted_at) AS bin1, count() AS rows, count() FILTER (WHERE n_mentions>0) AS with_mentions,
	138	+ sum(n_mentions) AS mentions, round(100.0count() FILTER (WHERE n_mentions=0)/count(*),2) AS zero_pct,
	139	+ count(*) FILTER (WHERE gate AND word_count>=30) AS calls,
	140	+ round(100.0count() FILTER (WHERE n_mentions=0 AND gate AND word_count>=30)/nullif(count(*) FILTER (WHERE gate AND word_count>=30),0),2) AS zero_pct_among_calls
	141	+FROM ledger WHERE run='october' GROUP BY 1 ORDER BY 1""")
	142	+B["october_distinct_timestamps"], B["october_rows_check"] = con.execute("select count(distinct extracted_at), count(*) from ledger where run='october'").fetchone()
	143	+B["timestamp_granularity_note"] = "extracted_at is stamped per row (executemany INSERT OR REPLACE, DEFAULT current_timestamp), not per flush: distinct timestamps = rows"
	144	+r.dump("flush_zero_groups_after_0622.csv", con, f"""
	145	+SELECT time_bucket(INTERVAL '1 minute', extracted_at) AS bin1, count() AS rows, count() FILTER (WHERE n_mentions=0) AS zeros,
	146	+ round(100.0count() FILTER (WHERE n_mentions=0)/count(*),2) AS zero_pct,
	147	+ count(*) FILTER (WHERE gate AND word_count>=30) AS calls,
	148	+ round(100.0count() FILTER (WHERE n_mentions=0 AND gate AND word_count>=30)/nullif(count(*) FILTER (WHERE gate AND word_count>=30),0),2) AS zero_pct_among_calls
	149	+FROM ledger WHERE run='october' AND extracted_at > TIMESTAMP '{OUTAGE_END}'
	150	+GROUP BY 1 HAVING count() >= 50 AND count() FILTER (WHERE n_mentions=0 AND gate AND word_count>=30) >= 0.99count() FILTER (WHERE gate AND word_count>=30) ORDER BY 1""")
	151	+# the same test before the outage, for comparison
	152	+r.dump("flush_zero_groups_before_0110.csv", con, f"""
	153	+SELECT time_bucket(INTERVAL '1 minute', extracted_at) AS bin1, count() AS rows, count() FILTER (WHERE n_mentions=0) AS zeros,
	154	+ round(100.0count() FILTER (WHERE n_mentions=0)/count(*),2) AS zero_pct,
	155	+ count(*) FILTER (WHERE gate AND word_count>=30) AS calls,
	156	+ round(100.0count() FILTER (WHERE n_mentions=0 AND gate AND word_count>=30)/nullif(count(*) FILTER (WHERE gate AND word_count>=30),0),2) AS zero_pct_among_calls
	157	+FROM ledger WHERE run='october' AND extracted_at < TIMESTAMP '{OUTAGE_START}'
	158	+GROUP BY 1 HAVING count() >= 50 AND count() FILTER (WHERE n_mentions=0 AND gate AND word_count>=30) >= 0.99count() FILTER (WHERE gate AND word_count>=30) ORDER BY 1""")
	159	+
	160	+# ---- (c) id collisions: ledger n_mentions > stored ------------------------------------------
	161	+r.dump("ledger_collisions.csv", con, """
	162	+SELECT section_id, accession_number, form_type, section_name AS item, cik, run, n_mentions AS ledger_n, stored_mentions AS stored_n,
	163	+ n_mentions - stored_mentions AS deficit, stored_keys
	164	+FROM ledger WHERE n_mentions > stored_mentions ORDER BY deficit DESC, section_id""")
	165	+r.dump("ledger_collisions_by_form.csv", con, """
	166	+SELECT form_type, count(*) AS sections, sum(n_mentions - stored_mentions) AS excess, sum(n_mentions) AS ledger_mentions, sum(stored_mentions) AS stored_mentions
	167	+FROM ledger WHERE n_mentions > stored_mentions GROUP BY 1 ORDER BY 1""")
	168	+C = {}
	169	+C["sections_ledger_gt_stored"], C["excess_mentions"] = con.execute("select count(*), sum(n_mentions-stored_mentions) from ledger where n_mentions > stored_mentions").fetchone()
	170	+C["sections_ledger_lt_stored"], C["deficit_mentions"] = con.execute("select count(*), sum(stored_mentions-n_mentions) from ledger where n_mentions < stored_mentions").fetchone()
	171	+C["sum_ledger_n_mentions"] = con.execute("select sum(n_mentions) from ledger").fetchone()[0]
	172	+C["stored_project_mentions"] = con.execute("select count(*) from project_mentions").fetchone()[0]
	173	+C["mentions_whose_section_not_in_ledger"] = con.execute("select count(*) from project_mentions m where not exists (select 1 from extracted_sections e where e.section_id=m.section_id)").fetchone()[0]
	174	+C["ledger_vs_stored_by_form"] = con.execute("select form_type, sum(n_mentions), sum(stored_mentions), sum(n_mentions)-sum(stored_mentions) from ledger group by 1 order by 1").fetchall()
	175	+C["duplicate_key_sections_in_stored"] = con.execute("""
	176	+ select count() from (select section_id from project_mentions group by section_id, project_type, left(project_name,40) having count()>1)""").fetchone()[0]
	177	+r.write_json("collisions.json", C)
	178	+
	179	+# ---- (f) misc code-vs-data checks ---------------------------------------------------------------
	180	+F = {}
	181	+F["distinct_backend_ledger"] = [x[0] for x in con.execute("select distinct backend from extracted_sections").fetchall()]
	182	+F["distinct_backend_mentions"] = [x[0] for x in con.execute("select distinct backend from project_mentions").fetchall()]
	183	+F["confidence_exact_0600_total"] = con.execute("select count(*) from project_mentions where round(confidence,3)=0.600").fetchone()[0]
	184	+F["confidence_exact_0600_by_run"] = con.execute("""
	185	+ select l.run, count(*) from project_mentions m join ledger l using(section_id) where round(m.confidence,3)=0.600 group by 1""").fetchall()
	186	+F["confidence_bins_by_run"] = con.execute("""
	187	+ select l.run, round(m.confidence,1) c, count(*) from project_mentions m join ledger l using(section_id) group by 1,2 order by 1,2""").fetchall()
	188	+F["min_confidence_by_run"] = con.execute("select l.run, min(m.confidence), max(m.confidence), count(*) from project_mentions m join ledger l using(section_id) group by 1").fetchall()
	189	+F["mentions_below_045"] = con.execute("select count(*) from project_mentions where confidence < 0.45").fetchone()[0]
	190	+F["amount_raw_rule_mismatch"] = con.execute("""
	191	+ select count(*) from project_mentions where amount_usd is not null and amount_raw is distinct from ('$' \|\| format('{:,.0f}', amount_usd))""").fetchone()[0]
	192	+F["amount_raw_rule_mismatch_rows"] = con.execute("""
	193	+ select mention_id, amount_usd, amount_raw from project_mentions where amount_usd is not null and amount_raw is distinct from ('$' \|\| format('{:,.0f}', amount_usd)) limit 20""").fetchall()
	194	+F["amount_raw_null_with_amount"] = con.execute("select count(*) from project_mentions where amount_usd is not null and amount_raw is null").fetchone()[0]
	195	+F["amount_raw_notnull_without_amount"] = con.execute("select count(*) from project_mentions where amount_usd is null and amount_raw is not null").fetchone()[0]
	196	+F["amount_le_0"] = con.execute("select count(*) from project_mentions where amount_usd <= 0").fetchone()[0]
	197	+F["mentions_with_run"] = con.execute("select l.run, count(*) from project_mentions m join ledger l using(section_id) group by 1").fetchall()
	198	+F["mentions_backfilled_block_vs_june_log"] = {"block_stored": B["block_stored_mentions"], "june_log": JUNE_LOG_MENTIONS}
	199	+F["fiscal_year_null_mentions_by_run"] = con.execute("select l.run, count() filter (where m.fiscal_year is null), count() from project_mentions m join ledger l using(section_id) group by 1").fetchall()
	200	+r.write_json("code_vs_data_checks.json", F)
	201	+
	202	+# ---- (e) throughput from the ledger ----------------------------------------------------------
	203	+E = {}
	204	+ts = con.execute("select distinct extracted_at from ledger where run='october' order by 1").fetchall()
	205	+ts = [t[0] for t in ts]
	206	+E["october_first_flush"] = str(ts[0]); E["october_last_flush"] = str(ts[-1])
	207	+E["october_distinct_flush_timestamps"] = len(ts)
	208	+E["october_rows"] = oct_rows
	209	+E["october_calls"] = oct_calls
	210	+wall = (ts[-1] - ts[0]).total_seconds()
	211	+gaps = sorted(((ts[i+1]-ts[i]).total_seconds(), str(ts[i]), str(ts[i+1])) for i in range(len(ts)-1))
	212	+E["largest_gaps_seconds"] = gaps[-5:]
	213	+outage_gap = gaps[-1][0]
	214	+E["wall_clock_hours_first_to_last_flush"] = round(wall/3600, 3)
	215	+E["largest_gap_hours"] = round(outage_gap/3600, 3)
	216	+E["effective_hours_excluding_largest_gap"] = round((wall-outage_gap)/3600, 3)
	217	+E["rows_per_second_wall"] = round(oct_rows/wall, 2)
	218	+E["rows_per_second_effective"] = round(oct_rows/(wall-outage_gap), 2)
	219	+E["calls_per_second_effective"] = round(oct_calls/(wall-outage_gap), 2)
	220	+E["paper_claim"] = {"sections": 968134, "hours": "about twelve", "rate": "27-36 sections per second", "workers": 48}
	221	+E["paper_rate_implied_by_12h"] = round(968134/(12*3600), 2)
	222	+# segment rates: before the outage and after
	223	+pre = con.execute(f"select count(*), min(extracted_at), max(extracted_at) from ledger where run='october' and extracted_at < TIMESTAMP '{OUTAGE_START}'").fetchone()
	224	+post = con.execute(f"select count(*), min(extracted_at), max(extracted_at) from ledger where run='october' and extracted_at > TIMESTAMP '{OUTAGE_END}'").fetchone()
	225	+E["segment_before_outage"] = {"rows": pre[0], "from": str(pre[1]), "to": str(pre[2]), "rows_per_s": round(pre[0]/max(1,(pre[2]-pre[1]).total_seconds()),2)}
	226	+E["segment_after_outage"] = {"rows": post[0], "from": str(post[1]), "to": str(post[2]), "rows_per_s": round(post[0]/max(1,(post[2]-post[1]).total_seconds()),2)}
	227	+E["rows_by_hour_october"] = con.execute("select date_trunc('hour', extracted_at) h, count(*) from ledger where run='october' group by 1 order by 1").fetchall()
	228	+r.write_json("throughput.json", E)
	229	+
	230	+# ---- (d) log parsers ---------------------------------------------------------------------------
	231	+LINE = re.compile(r"^(\d\d):(\d\d):(\d\d) (\w+)\s+(\S+) \\| (.*)$")
	232	+def parse_log(path: Path, start_date: dt.date):
	233	+ day = start_date; prev = None; out = []
	234	+ for raw in open(path, errors="replace"):
	235	+ m = LINE.match(raw.rstrip("\n"))
	236	+ if not m:
	237	+ out.append((None, None, None, raw.rstrip("\n"))); continue
	238	+ h, mi, s = int(m.group(1)), int(m.group(2)), int(m.group(3))
	239	+ t = dt.time(h, mi, s)
	240	+ if prev is not None and (dt.datetime.combine(day, prev) - dt.datetime.combine(day, t)).total_seconds() > 3600:
	241	+ day = day + dt.timedelta(days=1)
	242	+ prev = t
	243	+ out.append((dt.datetime.combine(day, t), m.group(4), m.group(5), m.group(6)))
	244	+ return out
	245	+
	246	+def classify(msg: str) -> str:
	247	+ if "retryable" in msg: return "retryable_http_429_5xx"
	248	+ if "Connection reset" in msg or "ConnectError" in msg or "ReadError" in msg or "Errno" in msg: return "connection_error"
	249	+ if "Unterminated string" in msg or "Expecting" in msg or "Extra data" in msg or "JSON" in msg: return "json_decode_error"
	250	+ if "KeyError" in msg: return "key_error"
	251	+ if "timed out" in msg.lower() or "Timeout" in msg: return "timeout"
	252	+ return "other"
	253	+
	254	+def summarize(path: Path, start_date: dt.date, window=None):
	255	+ rows = parse_log(path, start_date)
	256	+ S = {"path": str(path), "lines": len(rows), "first": str(next((r_[0] for r_ in rows if r_[0]), None)), "last": str(next((r_[0] for r_ in reversed(rows) if r_[0]), None))}
	257	+ fails = [(t, classify(msg)) for t, lvl, name, msg in rows if msg and "LLM call failed" in msg]
	258	+ S["llm_call_failed_total"] = len(fails)
	259	+ S["llm_call_failed_by_reason"] = dict(Counter(c for _, c in fails))
	260	+ byhour = Counter((t.strftime("%Y-%m-%d %H") if t else "?") for t, _ in fails)
	261	+ S["llm_call_failed_by_hour"] = dict(sorted(byhour.items()))
	262	+ if window:
	263	+ w0, w1 = window
	264	+ inside = [(t, c) for t, c in fails if t and w0 <= t <= w1]
	265	+ outside = [(t, c) for t, c in fails if not (t and w0 <= t <= w1)]
	266	+ S["window"] = [str(w0), str(w1)]
	267	+ S["llm_call_failed_inside_window"] = len(inside)
	268	+ S["llm_call_failed_outside_window"] = len(outside)
	269	+ S["outside_window_by_reason"] = dict(Counter(c for _, c in outside))
	270	+ S["outside_window_list"] = [(str(t), c) for t, c in outside]
	271	+ if fails:
	272	+ S["first_failure"] = str(min(t for t, _ in fails if t)); S["last_failure"] = str(max(t for t, _ in fails if t))
	273	+ ins = [t for t, _ in inside]
	274	+ if ins: S["first_failure_in_window"] = str(min(ins)); S["last_failure_in_window"] = str(max(ins))
	275	+ S["section_failed"] = sum(1 for t, lvl, name, msg in rows if msg and "section failed" in msg)
	276	+ S["batch_failed_lines"] = sum(1 for t, lvl, name, msg in rows if msg and "BATCH" in msg and "FAILED" in msg)
	277	+ S["pending_lines"] = [(str(t), msg) for t, lvl, name, msg in rows if msg and msg.startswith("pending:")]
	278	+ S["ciks_total_lines"] = [(str(t), msg) for t, lvl, name, msg in rows if msg and msg.startswith("CIKs total")]
	279	+ S["run_complete_lines"] = [(str(t), msg) for t, lvl, name, msg in rows if msg and ("RUN COMPLETE" in msg or "EXTRACT COMPLETE" in msg)]
	280	+ ext = [int(re.search(r"Extracting from (\d+) sections", msg).group(1)) for t, lvl, name, msg in rows if msg and "Extracting from" in msg]
	281	+ S["extracting_from_lines"] = len(ext); S["extracting_from_sum_sections"] = sum(ext)
	282	+ done = [(int(a), int(b)) for t, lvl, name, msg in rows for a, b in re.findall(r"Extraction complete: (\d+) mentions from (\d+) sections", msg or "")]
	283	+ S["extraction_complete_lines"] = len(done); S["extraction_complete_sum_sections"] = sum(b for a, b in done); S["extraction_complete_sum_mentions"] = sum(a for a, b in done)
	284	+ S["workers_values"] = sorted(set(re.findall(r"workers=(\d+)", "\n".join(msg or "" for *_, msg in rows))))
	285	+ S["batch_values"] = sorted(set(re.findall(r"batch=(\d+)", "\n".join(msg or "" for *_, msg in rows))))
	286	+ S["model_id_strings"] = sorted(set(re.findall(r"gpt-[\w\-\.]+", open(path, errors="replace").read())))
	287	+ return S, fails
	288	+
	289	+w0 = dt.datetime.fromisoformat(OUTAGE_START); w1 = dt.datetime.fromisoformat(OUTAGE_END)
	290	+oct_log, oct_fails = summarize(RDB / "data/logs/spid_extract.log", dt.date(2026, 10, 27), (w0, w1))
	291	+jun_log, jun_fails = summarize(RDB / "project-db/logs/extract.log", dt.date(2026, 6, 18), None)
	292	+# other October log files (small)
	293	+others = {}
	294	+for nm in ["spid.log", "spid_resume.log", "spid_post.log", "spid_interim.log"]:
	295	+ p = RDB / "data/logs" / nm
	296	+ if p.exists():
	297	+ txt = open(p, errors="replace").read()
	298	+ others[nm] = {"lines": txt.count("\n"), "head": txt[:1500]}
	299	+pend = RDB / "data/logs/spid_pending.json"
	300	+if pend.exists():
	301	+ try:
	302	+ pj = json.load(open(pend)); others["spid_pending.json"] = {k: (v if not isinstance(v, list) else v[:20]) for k, v in pj.items()} if isinstance(pj, dict) else str(pj)[:1500]
	303	+ except Exception as ex:
	304	+ others["spid_pending.json"] = str(ex)
	305	+# outage window measured from the log itself: the contiguous block of 'retryable' failures on 2026-10-28 between 01:00 and 07:00
	306	+retry_ts = [t for t, c in oct_fails if t and c == "retryable_http_429_5xx" and dt.datetime(2026,10,28,1,0) <= t <= dt.datetime(2026,10,28,7,0)]
	307	+oct_log["outage_window_from_log"] = [str(min(retry_ts)), str(max(retry_ts)), len(retry_ts)]
	308	+w0l, w1l = min(retry_ts), max(retry_ts)
	309	+oct_log["llm_call_failed_outside_log_window"] = [(str(t), c) for t, c in oct_fails if not (t and w0l <= t <= w1l)]
	310	+oct_log["silent_empty_results_outside_outage"] = len(oct_log["llm_call_failed_outside_log_window"])
	311	+oct_log["retryable_failures_outside_log_window"] = sum(1 for t, c in oct_fails if c == "retryable_http_429_5xx" and not (t and w0l <= t <= w1l))
	312	+oct_log["ledger_rows_in_log_window"] = con.execute(f"select count(*), sum(n_mentions) from ledger where extracted_at between TIMESTAMP '{w0l}' and TIMESTAMP '{w1l}'").fetchone()
	313	+oct_log["ledger_rows_in_largest_gap"] = con.execute(f"select count(*) from ledger where extracted_at > TIMESTAMP '{gaps[-1][1]}' and extracted_at < TIMESTAMP '{gaps[-1][2]}'").fetchone()[0]
	314	+pend2 = int(re.search(r"pending: (\d+)", oct_log["pending_lines"][1][1]).group(1)) if len(oct_log["pending_lines"]) > 1 else None
	315	+oct_log["outage_rows_reconciliation"] = {"extraction_complete_sum_sections": oct_log["extraction_complete_sum_sections"], "pending_at_second_start": pend2,
	316	+ "difference": (oct_log["extraction_complete_sum_sections"] - pend2) if pend2 else None, "paper_research_log_149352": 149352,
	317	+ "pending_at_resume": int(re.search(r"pending: (\d+)", oct_log["pending_lines"][-1][1]).group(1)),
	318	+ "rows_before_outage_plus_in_window": con.execute(f"select count(*) from ledger where run='october' and extracted_at < TIMESTAMP '{gaps[-1][2]}'").fetchone()[0]}
	319	+# batch-level productivity from the log (silent-failure test, THEO-05): mentions per section per batch
	320	+def batch_rows(rows_, run_):
	321	+ out = []
	322	+ for t, lvl, name, msg in rows_:
	323	+ m_ = re.search(r"BATCH (\d+)/(\d+) done \\| batch_sec=(\d+) batch_men=(\d+)", msg or "")
	324	+ if m_:
	325	+ out.append((run_, int(m_.group(1)), int(m_.group(2)), str(t), int(m_.group(3)), int(m_.group(4)), round(int(m_.group(4))/max(1,int(m_.group(3))), 4)))
	326	+ return out
	327	+oct_rows_ = parse_log(RDB / "data/logs/spid_extract.log", dt.date(2026, 10, 27)); jun_rows_ = parse_log(RDB / "project-db/logs/extract.log", dt.date(2026, 6, 18))
	328	+br = batch_rows(oct_rows_, "october") + batch_rows(jun_rows_, "june")
	329	+import statistics
	330	+med_oct = statistics.median([b[6] for b in br if b[0] == "october"]); med_jun = statistics.median([b[6] for b in br if b[0] == "june"])
	331	+br2 = [b + (round(b[6]/(med_oct if b[0]=="october" else med_jun), 3), (b[6] < 0.25*(med_oct if b[0]=="october" else med_jun))) for b in br]
	332	+r.write_csv("batch_productivity_from_log.csv", ["run", "batch", "n_batches", "done_at", "sections", "mentions", "mentions_per_section", "ratio_to_median", "anomalous_lt_25pct_of_median"], br2)
	333	+oct_log["batch_productivity_median_mentions_per_section"] = med_oct
	334	+oct_log["anomalous_batches"] = [b for b in br2 if b[0] == "october" and b[8]]
	335	+jun_log["batch_productivity_median_mentions_per_section"] = med_jun
	336	+jun_log["anomalous_batches"] = [b for b in br2 if b[0] == "june" and b[8]]
	337	+# the first resumed batch: rows stamped between its start and end, with the expected productive count at the form x item rates of the rest of the October run
	338	+b1 = [b for b in br2 if b[0] == "october" and b[1] == 1 and b[2] == 81]
	339	+if b1:
	340	+ b1_end = b1[0][3]
	341	+ b1_start = str(dt.datetime.fromisoformat(oct_log["pending_lines"][-1][0]))
	342	+ con.execute(f"CREATE TEMP TABLE b1 AS SELECT * FROM ledger WHERE run='october' AND extracted_at BETWEEN TIMESTAMP '{b1_start}' AND TIMESTAMP '{b1_end}'")
	343	+ r.dump("resume_batch1_vs_expected.csv", con, """
	344	+ WITH rates AS (SELECT form_type, section_name, count() FILTER (WHERE n_mentions>0)1.0/nullif(count() FILTER (WHERE gate AND word_count>=30),0) p_prod, sum(n_mentions)1.0/nullif(count(*) FILTER (WHERE gate AND word_count>=30),0) m_per_call
	345	+ FROM ledger WHERE run='october' AND section_id NOT IN (SELECT section_id FROM b1) GROUP BY 1,2)
	346	+ SELECT b.form_type, b.section_name AS item, count() rows, count() FILTER (WHERE gate AND word_count>=30) calls, count(*) FILTER (WHERE n_mentions>0) with_mentions, sum(n_mentions) mentions,
	347	+ round(sum(CASE WHEN gate AND word_count>=30 THEN r.p_prod ELSE 0 END),1) expected_with_mentions, round(sum(CASE WHEN gate AND word_count>=30 THEN r.m_per_call ELSE 0 END),1) expected_mentions,
	348	+ count(DISTINCT b.cik) ciks
	349	+ FROM b1 b LEFT JOIN rates r ON b.form_type=r.form_type AND b.section_name=r.section_name GROUP BY 1,2 ORDER BY 1,2""")
	350	+ oct_log["resume_batch1"] = {"window": [b1_start, b1_end], "log_sections": b1[0][4], "log_mentions": b1[0][5],
	351	+ "ledger_rows": con.execute("select count(*) from b1").fetchone()[0],
	352	+ "ledger_calls": con.execute("select count(*) from b1 where gate and word_count>=30").fetchone()[0],
	353	+ "ledger_with_mentions": con.execute("select count(*) from b1 where n_mentions>0").fetchone()[0],
	354	+ "ledger_mentions": con.execute("select sum(n_mentions) from b1").fetchone()[0],
	355	+ "ciks": con.execute("select list(distinct cik order by cik) from b1").fetchone()[0],
	356	+ "expected_with_mentions": con.execute("""WITH rates AS (SELECT form_type, section_name, count() FILTER (WHERE n_mentions>0)1.0/nullif(count(*) FILTER (WHERE gate AND word_count>=30),0) p FROM ledger WHERE run='october' AND section_id NOT IN (SELECT section_id FROM b1) GROUP BY 1,2)
	357	+ SELECT round(sum(CASE WHEN gate AND word_count>=30 THEN r.p ELSE 0 END),1) FROM b1 b LEFT JOIN rates r ON b.form_type=r.form_type AND b.section_name=r.section_name""").fetchone()[0],
	358	+ "failed_call_lines_in_window": sum(1 for t, c in oct_fails if t and dt.datetime.fromisoformat(b1_start) <= t <= dt.datetime.fromisoformat(b1_end))}
	359	+r.write_json("log_summary.json", {"october": oct_log, "june": jun_log, "other_logs": others,
	360	+ "june_log_date_assumed": "2026-06-18 (file mtime; the log carries time-of-day only)"})
	361	+r.write_csv("log_failures_by_hour.csv", ["run", "hour", "reason", "n"],
	362	+ [("october", h, c, n) for (h, c), n in sorted(Counter((t.strftime("%Y-%m-%d %H") if t else "?", c) for t, c in oct_fails).items())] +
	363	+ [("june", h, c, n) for (h, c), n in sorted(Counter((t.strftime("%Y-%m-%d %H") if t else "?", c) for t, c in jun_fails).items())])
	364	+
	365	+# ---- Table 3 replacement input ---------------------------------------------------------------
	366	+r.dump("table3_ledger_vs_stored.csv", con, """
	367	+SELECT form_type, count() AS ledger_rows, count() FILTER (WHERE gate AND word_count>=30) AS calls,
	368	+ count(*) FILTER (WHERE n_mentions>0) AS with_mentions, sum(n_mentions) AS returned_by_model_before_dedup,
	369	+ sum(stored_mentions) AS stored_mentions, count(DISTINCT cik) AS issuers,
	370	+ count(DISTINCT cik) FILTER (WHERE n_mentions>0) AS issuers_with_mentions
	371	+FROM ledger GROUP BY 1 ORDER BY 1""")
	372	+r.notes["issuers_total_distinct_in_ledger"] = con.execute("select count(distinct cik) from ledger").fetchone()[0]
	373	+r.notes["issuers_with_filings"] = con.execute("select count(distinct cik) from src_filings").fetchone()[0]
	374	+r.finish()

added results/ledger/backfill_block.json +132 −0

		`@@ -0,0 +1,132 @@`
	1	+{
	2	+ "min_extracted_at": "2026-10-27 13:12:32.140400",
	3	+ "rows_at_min_timestamp": 154964,
	4	+ "june_log_sections": 153894,
	5	+ "gap_rows_minus_log": 1070,
	6	+ "block_ledger_mentions": 39930,
	7	+ "block_stored_mentions": 39930,
	8	+ "june_log_mentions": 39930,
	9	+ "block_lt30_words": 1070,
	10	+ "block_gate_fail": 59666,
	11	+ "block_lt30_or_gate_fail": 59678,
	12	+ "block_ge30_and_gate": 95286,
	13	+ "october_lt30_words": 0,
	14	+ "block_by_form": [
	15	+ [
	16	+ "10-K",
	17	+ 27164,
	18	+ 15787,
	19	+ 0
	20	+ ],
	21	+ [
	22	+ "10-Q",
	23	+ 23873,
	24	+ 5817,
	25	+ 1069
	26	+ ],
	27	+ [
	28	+ "8-K",
	29	+ 103927,
	30	+ 18326,
	31	+ 1
	32	+ ]
	33	+ ],
	34	+ "block_by_segmenter": [
	35	+ [
	36	+ "parsed",
	37	+ 127800
	38	+ ],
	39	+ [
	40	+ "legacy_ingest",
	41	+ 27164
	42	+ ]
	43	+ ],
	44	+ "block_sp500_share": [
	45	+ 154964,
	46	+ 154964
	47	+ ],
	48	+ "block_ciks": 500,
	49	+ "outage_window": [
	50	+ "2026-10-28 01:10:00",
	51	+ "2026-10-28 06:22:00"
	52	+ ],
	53	+ "rows_in_outage_window": [
	54	+ 504,
	55	+ 1011
	56	+ ],
	57	+ "rows_0110_0640": [
	58	+ 5273,
	59	+ 1041
	60	+ ],
	61	+ "rows_by_hour_1028": [
	62	+ [
	63	+ 0,
	64	+ 107883,
	65	+ 92099,
	66	+ 30321
	67	+ ],
	68	+ [
	69	+ 1,
	70	+ 16609,
	71	+ 12953,
	72	+ 7061
	73	+ ],
	74	+ [
	75	+ 6,
	76	+ 35147,
	77	+ 31638,
	78	+ 7092
	79	+ ],
	80	+ [
	81	+ 7,
	82	+ 148277,
	83	+ 125987,
	84	+ 41887
	85	+ ],
	86	+ [
	87	+ 8,
	88	+ 114988,
	89	+ 98154,
	90	+ 32310
	91	+ ],
	92	+ [
	93	+ 9,
	94	+ 130711,
	95	+ 111241,
	96	+ 36466
	97	+ ],
	98	+ [
	99	+ 10,
	100	+ 93253,
	101	+ 77492,
	102	+ 29917
	103	+ ],
	104	+ [
	105	+ 11,
	106	+ 22048,
	107	+ 17903,
	108	+ 7103
	109	+ ]
	110	+ ],
	111	+ "rows_by_hour_1027": [
	112	+ [
	113	+ 21,
	114	+ 56405,
	115	+ 48434,
	116	+ 14344
	117	+ ],
	118	+ [
	119	+ 22,
	120	+ 126305,
	121	+ 107801,
	122	+ 33512
	123	+ ],
	124	+ [
	125	+ 23,
	126	+ 116508,
	127	+ 100609,
	128	+ 28534
	129	+ ]
	130	+ ],
	131	+ "paper_outage_rows_149352": 149352
	132	+}
		\ No newline at end of file

added results/ledger/batch_productivity_from_log.csv +176 −0

		`@@ -0,0 +1,176 @@`
	1	+run,batch,n_batches,done_at,sections,mentions,mentions_per_section,ratio_to_median,anomalous_lt_25pct_of_median
	2	+october,1,125,2026-10-27 21:41:00,8596,1902,0.2213,0.795,False
	3	+october,2,125,2026-10-27 21:44:12,8723,2721,0.3119,1.12,False
	4	+october,3,125,2026-10-27 21:46:33,9571,2116,0.2211,0.794,False
	5	+october,4,125,2026-10-27 21:49:32,9102,2562,0.2815,1.011,False
	6	+october,5,125,2026-10-27 21:52:48,8408,1951,0.232,0.833,False
	7	+october,6,125,2026-10-27 21:58:36,8984,2089,0.2325,0.835,False
	8	+october,7,125,2026-10-27 22:03:29,8954,2939,0.3282,1.179,False
	9	+october,8,125,2026-10-27 22:08:22,8299,2793,0.3365,1.209,False
	10	+october,9,125,2026-10-27 22:12:28,8588,2371,0.2761,0.992,False
	11	+october,10,125,2026-10-27 22:17:45,10363,2742,0.2646,0.95,False
	12	+october,11,125,2026-10-27 22:22:29,7837,2227,0.2842,1.021,False
	13	+october,12,125,2026-10-27 22:25:14,9610,2085,0.217,0.779,False
	14	+october,13,125,2026-10-27 22:28:24,8549,2478,0.2899,1.041,False
	15	+october,14,125,2026-10-27 22:31:08,7599,1906,0.2508,0.901,False
	16	+october,15,125,2026-10-27 22:35:00,9094,2520,0.2771,0.995,False
	17	+october,16,125,2026-10-27 22:37:41,8437,2367,0.2805,1.008,False
	18	+october,17,125,2026-10-27 22:40:13,8233,2272,0.276,0.991,False
	19	+october,18,125,2026-10-27 22:45:53,9705,2410,0.2483,0.892,False
	20	+october,19,125,2026-10-27 22:51:46,10801,2329,0.2156,0.774,False
	21	+october,20,125,2026-10-27 22:57:49,10564,2575,0.2438,0.876,False
	22	+october,21,125,2026-10-27 23:03:05,10945,2124,0.1941,0.697,False
	23	+october,22,125,2026-10-27 23:08:21,8674,2074,0.2391,0.859,False
	24	+october,23,125,2026-10-27 23:13:38,9460,2412,0.255,0.916,False
	25	+october,24,125,2026-10-27 23:17:51,10644,2469,0.232,0.833,False
	26	+october,25,125,2026-10-27 23:23:56,9128,2503,0.2742,0.985,False
	27	+october,26,125,2026-10-27 23:29:07,9330,2779,0.2979,1.07,False
	28	+october,27,125,2026-10-27 23:37:16,10209,2387,0.2338,0.84,False
	29	+october,28,125,2026-10-27 23:41:09,10831,2241,0.2069,0.743,False
	30	+october,29,125,2026-10-27 23:44:30,9162,2785,0.304,1.092,False
	31	+october,30,125,2026-10-27 23:49:41,9476,2326,0.2455,0.882,False
	32	+october,31,125,2026-10-27 23:54:22,10573,2314,0.2189,0.786,False
	33	+october,32,125,2026-10-27 23:58:21,9135,2116,0.2316,0.832,False
	34	+october,33,125,2026-10-28 00:04:07,9893,3113,0.3147,1.13,False
	35	+october,34,125,2026-10-28 00:08:14,9143,2574,0.2815,1.011,False
	36	+october,35,125,2026-10-28 00:15:29,10740,3197,0.2977,1.069,False
	37	+october,36,125,2026-10-28 00:20:57,11636,3241,0.2785,1.0,False
	38	+october,37,125,2026-10-28 00:27:11,9545,2754,0.2885,1.036,False
	39	+october,38,125,2026-10-28 00:35:17,9948,2921,0.2936,1.055,False
	40	+october,39,125,2026-10-28 00:42:28,11222,3109,0.277,0.995,False
	41	+october,40,125,2026-10-28 00:46:24,9764,2819,0.2887,1.037,False
	42	+october,41,125,2026-10-28 00:52:03,9792,2612,0.2667,0.958,False
	43	+october,42,125,2026-10-28 00:57:42,11237,2485,0.2211,0.794,False
	44	+october,43,125,2026-10-28 01:00:46,8950,2712,0.303,1.088,False
	45	+october,44,125,2026-10-28 01:05:51,8611,2897,0.3364,1.208,False
	46	+october,45,125,2026-10-28 01:09:50,10332,2442,0.2364,0.849,False
	47	+october,46,125,2026-10-28 01:25:38,10260,1011,0.0985,0.354,False
	48	+october,47,125,2026-10-28 01:44:26,9550,0,0.0,0.0,True
	49	+october,48,125,2026-10-28 02:02:20,8726,0,0.0,0.0,True
	50	+october,49,125,2026-10-28 02:25:08,10693,0,0.0,0.0,True
	51	+october,50,125,2026-10-28 02:45:05,9784,0,0.0,0.0,True
	52	+october,51,125,2026-10-28 03:06:35,10260,0,0.0,0.0,True
	53	+october,52,125,2026-10-28 03:24:01,9081,0,0.0,0.0,True
	54	+october,53,125,2026-10-28 03:47:33,10765,0,0.0,0.0,True
	55	+october,54,125,2026-10-28 04:05:52,8367,0,0.0,0.0,True
	56	+october,55,125,2026-10-28 04:29:31,10903,0,0.0,0.0,True
	57	+october,56,125,2026-10-28 04:47:54,9042,0,0.0,0.0,True
	58	+october,57,125,2026-10-28 05:07:03,9047,0,0.0,0.0,True
	59	+october,58,125,2026-10-28 05:24:38,8672,0,0.0,0.0,True
	60	+october,59,125,2026-10-28 05:45:11,10171,0,0.0,0.0,True
	61	+october,60,125,2026-10-28 06:05:10,9337,0,0.0,0.0,True
	62	+october,1,81,2026-10-28 06:40:53,5198,34,0.0065,0.023,True
	63	+october,2,81,2026-10-28 06:46:05,9756,1931,0.1979,0.711,False
	64	+october,3,81,2026-10-28 06:51:56,9550,2749,0.2879,1.034,False
	65	+october,4,81,2026-10-28 06:58:08,8726,1877,0.2151,0.773,False
	66	+october,5,81,2026-10-28 07:03:04,10693,3043,0.2846,1.022,False
	67	+october,6,81,2026-10-28 07:07:54,9784,2259,0.2309,0.829,False
	68	+october,7,81,2026-10-28 07:13:48,10260,2884,0.2811,1.01,False
	69	+october,8,81,2026-10-28 07:16:26,9081,2827,0.3113,1.118,False
	70	+october,9,81,2026-10-28 07:20:31,10765,3159,0.2935,1.054,False
	71	+october,10,81,2026-10-28 07:23:33,8367,2630,0.3143,1.129,False
	72	+october,11,81,2026-10-28 07:26:57,10903,3031,0.278,0.999,False
	73	+october,12,81,2026-10-28 07:30:15,9042,2387,0.264,0.948,False
	74	+october,13,81,2026-10-28 07:33:28,9047,2270,0.2509,0.901,False
	75	+october,14,81,2026-10-28 07:36:25,8672,2850,0.3286,1.18,False
	76	+october,15,81,2026-10-28 07:42:11,10171,3576,0.3516,1.263,False
	77	+october,16,81,2026-10-28 07:45:36,9337,2977,0.3188,1.145,False
	78	+october,17,81,2026-10-28 07:48:50,10088,2603,0.258,0.927,False
	79	+october,18,81,2026-10-28 07:52:13,10182,2359,0.2317,0.832,False
	80	+october,19,81,2026-10-28 07:56:34,9101,2533,0.2783,1.0,False
	81	+october,20,81,2026-10-28 08:02:06,10155,2515,0.2477,0.89,False
	82	+october,21,81,2026-10-28 08:08:53,9509,2811,0.2956,1.062,False
	83	+october,22,81,2026-10-28 08:12:58,7641,2316,0.3031,1.089,False
	84	+october,23,81,2026-10-28 08:16:15,10020,3346,0.3339,1.199,False
	85	+october,24,81,2026-10-28 08:21:04,11340,3469,0.3059,1.099,False
	86	+october,25,81,2026-10-28 08:25:46,9397,2494,0.2654,0.953,False
	87	+october,26,81,2026-10-28 08:29:08,8219,2316,0.2818,1.012,False
	88	+october,27,81,2026-10-28 08:31:50,8764,2560,0.2921,1.049,False
	89	+october,28,81,2026-10-28 08:37:51,10213,2003,0.1961,0.704,False
	90	+october,29,81,2026-10-28 08:42:17,9579,2587,0.2701,0.97,False
	91	+october,30,81,2026-10-28 08:48:43,8960,2706,0.302,1.085,False
	92	+october,31,81,2026-10-28 08:54:30,8143,2185,0.2683,0.964,False
	93	+october,32,81,2026-10-28 09:00:11,8517,2245,0.2636,0.947,False
	94	+october,33,81,2026-10-28 09:03:35,8343,1974,0.2366,0.85,False
	95	+october,34,81,2026-10-28 09:07:52,9979,2354,0.2359,0.847,False
	96	+october,35,81,2026-10-28 09:10:23,6747,2056,0.3047,1.094,False
	97	+october,36,81,2026-10-28 09:13:53,8212,2188,0.2664,0.957,False
	98	+october,37,81,2026-10-28 09:18:52,8843,2341,0.2647,0.951,False
	99	+october,38,81,2026-10-28 09:23:12,8215,2785,0.339,1.218,False
	100	+october,39,81,2026-10-28 09:25:11,6103,1826,0.2992,1.075,False
	101	+october,40,81,2026-10-28 09:28:10,7599,2260,0.2974,1.068,False
	102	+october,41,81,2026-10-28 09:31:25,7284,1748,0.24,0.862,False
	103	+october,42,81,2026-10-28 09:34:27,7932,2443,0.308,1.106,False
	104	+october,43,81,2026-10-28 09:38:08,7207,2017,0.2799,1.005,False
	105	+october,44,81,2026-10-28 09:42:49,7914,2579,0.3259,1.171,False
	106	+october,45,81,2026-10-28 09:46:04,6854,1646,0.2402,0.863,False
	107	+october,46,81,2026-10-28 09:50:02,6570,1791,0.2726,0.979,False
	108	+october,47,81,2026-10-28 09:53:03,7197,1793,0.2491,0.895,False
	109	+october,48,81,2026-10-28 09:55:57,6578,1801,0.2738,0.983,False
	110	+october,49,81,2026-10-28 09:58:07,5730,1619,0.2825,1.015,False
	111	+october,50,81,2026-10-28 10:01:00,5955,2388,0.401,1.44,False
	112	+october,51,81,2026-10-28 10:03:55,5620,1704,0.3032,1.089,False
	113	+october,52,81,2026-10-28 10:07:22,5253,1400,0.2665,0.957,False
	114	+october,53,81,2026-10-28 10:10:27,5477,2024,0.3695,1.327,False
	115	+october,54,81,2026-10-28 10:16:01,5702,2058,0.3609,1.296,False
	116	+october,55,81,2026-10-28 10:19:52,4770,1385,0.2904,1.043,False
	117	+october,56,81,2026-10-28 10:22:27,5305,1509,0.2844,1.022,False
	118	+october,57,81,2026-10-28 10:24:37,5088,1528,0.3003,1.079,False
	119	+october,58,81,2026-10-28 10:26:47,5977,1825,0.3053,1.097,False
	120	+october,59,81,2026-10-28 10:28:56,4767,1441,0.3023,1.086,False
	121	+october,60,81,2026-10-28 10:32:08,4142,1324,0.3197,1.148,False
	122	+october,61,81,2026-10-28 10:36:09,4916,1681,0.3419,1.228,False
	123	+october,62,81,2026-10-28 10:38:19,4104,1178,0.287,1.031,False
	124	+october,63,81,2026-10-28 10:39:44,3576,1313,0.3672,1.319,False
	125	+october,64,81,2026-10-28 10:42:00,3981,1041,0.2615,0.939,False
	126	+october,65,81,2026-10-28 10:45:01,4153,1156,0.2784,1.0,False
	127	+october,66,81,2026-10-28 10:47:13,3751,1651,0.4401,1.581,False
	128	+october,67,81,2026-10-28 10:50:35,4014,1428,0.3558,1.278,False
	129	+october,68,81,2026-10-28 10:54:21,3813,1189,0.3118,1.12,False
	130	+october,69,81,2026-10-28 10:57:53,3788,1195,0.3155,1.133,False
	131	+october,70,81,2026-10-28 11:00:19,3557,1092,0.307,1.103,False
	132	+october,71,81,2026-10-28 11:02:03,3352,929,0.2771,0.995,False
	133	+october,72,81,2026-10-28 11:04:10,2644,782,0.2958,1.062,False
	134	+october,73,81,2026-10-28 11:06:07,2468,880,0.3566,1.281,False
	135	+october,74,81,2026-10-28 11:08:58,2751,924,0.3359,1.207,False
	136	+october,75,81,2026-10-28 11:10:53,2431,938,0.3858,1.386,False
	137	+october,76,81,2026-10-28 11:12:55,1925,550,0.2857,1.026,False
	138	+october,77,81,2026-10-28 11:14:03,1636,575,0.3515,1.263,False
	139	+october,78,81,2026-10-28 11:14:41,1352,421,0.3114,1.119,False
	140	+october,79,81,2026-10-28 11:15:07,962,291,0.3025,1.087,False
	141	+october,80,81,2026-10-28 11:15:32,499,150,0.3006,1.08,False
	142	+october,81,81,2026-10-28 11:15:49,208,72,0.3462,1.244,False
	143	+june,1,34,2026-06-18 08:31:49,4271,997,0.2334,0.895,False
	144	+june,2,34,2026-06-18 08:36:05,4831,1118,0.2314,0.888,False
	145	+june,3,34,2026-06-18 08:40:41,4476,1326,0.2962,1.136,False
	146	+june,4,34,2026-06-18 08:44:50,4379,1155,0.2638,1.012,False
	147	+june,5,34,2026-06-18 08:48:37,4022,901,0.224,0.859,False
	148	+june,6,34,2026-06-18 08:53:37,6315,1178,0.1865,0.715,False
	149	+june,7,34,2026-06-18 08:56:53,4460,835,0.1872,0.718,False
	150	+june,8,34,2026-06-18 09:03:36,6573,1848,0.2812,1.079,False
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	153	+june,11,34,2026-06-18 09:17:47,4619,1225,0.2652,1.017,False
	154	+june,12,34,2026-06-18 09:24:11,6177,1750,0.2833,1.087,False
	155	+june,13,34,2026-06-18 09:29:01,4633,1302,0.281,1.078,False
	156	+june,14,34,2026-06-18 09:32:59,4464,944,0.2115,0.811,False
	157	+june,15,34,2026-06-18 09:38:18,4469,1619,0.3623,1.39,False
	158	+june,16,34,2026-06-18 09:43:23,4734,1188,0.251,0.963,False
	159	+june,17,34,2026-06-18 09:47:38,4186,1028,0.2456,0.942,False
	160	+june,18,34,2026-06-18 09:54:22,6003,1721,0.2867,1.1,False
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	162	+june,20,34,2026-06-18 10:04:17,4784,964,0.2015,0.773,False
	163	+june,21,34,2026-06-18 10:09:03,4083,1181,0.2892,1.109,False
	164	+june,22,34,2026-06-18 10:13:49,4535,1183,0.2609,1.001,False
	165	+june,23,34,2026-06-18 10:20:06,4998,1557,0.3115,1.195,False
	166	+june,24,34,2026-06-18 10:26:33,5451,1587,0.2911,1.117,False
	167	+june,25,34,2026-06-18 10:32:52,4960,1495,0.3014,1.156,False
	168	+june,26,34,2026-06-18 10:39:52,5185,1796,0.3464,1.329,False
	169	+june,27,34,2026-06-18 10:44:31,4780,977,0.2044,0.784,False
	170	+june,28,34,2026-06-18 10:50:19,5934,1410,0.2376,0.911,False
	171	+june,29,34,2026-06-18 10:54:16,3938,1026,0.2605,0.999,False
	172	+june,30,34,2026-06-18 10:58:30,4361,947,0.2172,0.833,False
	173	+june,31,34,2026-06-18 11:02:06,3019,850,0.2816,1.08,False
	174	+june,32,34,2026-06-18 11:05:01,2293,624,0.2721,1.044,False
	175	+june,33,34,2026-06-18 11:06:28,1268,323,0.2547,0.977,False
	176	+june,34,34,2026-06-18 11:06:44,116,47,0.4052,1.554,False

added results/ledger/code_vs_data_checks.json +228 −0

		`@@ -0,0 +1,228 @@`
	1	+{
	2	+ "distinct_backend_ledger": [
	3	+ "llm"
	4	+ ],
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	212	+ "mentions_backfilled_block_vs_june_log": {
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	214	+ "june_log": 39930
	215	+ },
	216	+ "fiscal_year_null_mentions_by_run": [
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	228	+}
		\ No newline at end of file

added results/ledger/collisions.json +30 −0

		`@@ -0,0 +1,30 @@`
	1	+{
	2	+ "sections_ledger_gt_stored": 304,
	3	+ "excess_mentions": 412,
	4	+ "sections_ledger_lt_stored": 0,
	5	+ "deficit_mentions": null,
	6	+ "sum_ledger_n_mentions": 308477,
	7	+ "stored_project_mentions": 308065,
	8	+ "mentions_whose_section_not_in_ledger": 0,
	9	+ "ledger_vs_stored_by_form": [
	10	+ [
	11	+ "10-K",
	12	+ 81622,
	13	+ 81495,
	14	+ 127
	15	+ ],
	16	+ [
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	22	+ [
	23	+ "8-K",
	24	+ 74594,
	25	+ 74566,
	26	+ 28
	27	+ ]
	28	+ ],
	29	+ "duplicate_key_sections_in_stored": 0
	30	+}
		\ No newline at end of file

added results/ledger/flush_zero_groups_after_0622.csv +2 −0

		`@@ -0,0 +1,2 @@`
	1	+bin1,rows,zeros,zero_pct,calls,zero_pct_among_calls
	2	+2026-10-28 06:40:00,5198,5173,99.52,2954,99.15

added results/ledger/flush_zero_groups_before_0110.csv +1 −0

		`@@ -0,0 +1 @@`
	1	+bin1,rows,zeros,zero_pct,calls,zero_pct_among_calls

added results/ledger/gate_pass_by_form.csv +4 −0

		`@@ -0,0 +1,4 @@`
	1	+form_type,ledger_rows,lt30_words,gate_pass,calls,gate_rejected_zero,gate_fail_but_mentions,with_mentions,ledger_mentions,stored_mentions,issuers,productive_pct_ledger,productive_pct_calls,mentions_per_ledger_row,mentions_per_call
	2	+10-K,143704,0,135310,135310,8394,0,30877,81622,81495,2464,21.49,22.82,0.568,0.6032
	3	+10-Q,578793,1069,363985,363973,214808,0,75705,152261,152004,2494,13.08,20.8,0.2631,0.4183
	4	+8-K,400601,1,204442,204442,196159,0,61648,74594,74566,2499,15.39,30.15,0.1862,0.3649

added results/ledger/gate_pass_by_form_item.csv +29 −0

		`@@ -0,0 +1,29 @@`
	1	+form_type,item,run,ledger_rows,lt30_words,gate_pass,calls,gate_rejected_zero,gate_fail_but_mentions,with_mentions,ledger_mentions,stored_mentions,issuers,productive_pct_ledger,productive_pct_calls,mentions_per_ledger_row,mentions_per_call
	2	+10-K,business,backfilled_june,5545,0,5542,5542,3,0,2185,5934,5934,460,39.4,39.43,1.0702,1.0707
	3	+10-K,business,october,23740,0,23576,23576,164,0,8383,24558,24521,2095,35.31,35.56,1.0345,1.0417
	4	+10-K,full_body,backfilled_june,1230,0,1229,1229,1,0,316,824,824,230,25.69,25.71,0.6699,0.6705
	5	+10-K,md_and_a,backfilled_june,5198,0,5096,5096,102,0,2505,6703,6703,459,48.19,49.16,1.2895,1.3153
	6	+10-K,md_and_a,october,23721,0,22954,22954,767,0,11779,32396,32318,2074,49.66,51.32,1.3657,1.4113
	7	+10-K,properties,backfilled_june,5084,0,4897,4897,187,0,870,1721,1721,444,17.11,17.77,0.3385,0.3514
	8	+10-K,properties,october,23152,0,21043,21043,2109,0,3013,6158,6148,2035,13.01,14.32,0.266,0.2926
	9	+10-K,quantitative_disclosures,backfilled_june,4502,0,3992,3992,510,0,71,110,110,405,1.58,1.78,0.0244,0.0276
	10	+10-K,quantitative_disclosures,october,22374,0,18040,18040,4334,0,284,421,420,2041,1.27,1.57,0.0188,0.0233
	11	+10-K,risk_factors,backfilled_june,5605,0,5580,5580,25,0,257,495,495,469,4.59,4.61,0.0883,0.0887
	12	+10-K,risk_factors,october,23553,0,23361,23361,192,0,1214,2302,2301,2079,5.15,5.2,0.0977,0.0985
	13	+10-Q,controls_and_procedures,backfilled_june,3567,1,797,797,2770,0,280,338,338,82,7.85,35.13,0.0948,0.4241
	14	+10-Q,controls_and_procedures,october,88826,0,20116,20116,68710,0,5323,6744,6741,2478,5.99,26.46,0.0759,0.3353
	15	+10-Q,financial_statements,backfilled_june,3594,0,3507,3507,87,0,743,1183,1183,82,20.67,21.19,0.3292,0.3373
	16	+10-Q,financial_statements,october,88288,0,86599,86599,1689,0,18266,28976,28897,2462,20.69,21.09,0.3282,0.3346
	17	+10-Q,full_document,october,241,0,241,241,0,0,38,107,107,12,15.77,15.77,0.444,0.444
	18	+10-Q,legal_proceedings,backfilled_june,3024,405,956,944,2068,0,18,20,20,82,0.6,1.91,0.0066,0.0212
	19	+10-Q,legal_proceedings,october,63974,0,19087,19087,44887,0,623,825,825,2251,0.97,3.26,0.0129,0.0432
	20	+10-Q,md_and_a,backfilled_june,3606,4,3599,3599,7,0,1563,3791,3791,83,43.34,43.43,1.0513,1.0533
	21	+10-Q,md_and_a,october,87765,0,86670,86670,1095,0,38224,95514,95376,2465,43.55,44.1,1.0883,1.102
	22	+10-Q,quantitative_disclosures,backfilled_june,3394,234,1662,1662,1732,0,37,51,51,81,1.09,2.23,0.015,0.0307
	23	+10-Q,quantitative_disclosures,october,79744,0,45058,45058,34686,0,553,757,749,2394,0.69,1.23,0.0095,0.0168
	24	+10-Q,risk_factors,backfilled_june,3344,425,1200,1200,2144,0,106,150,150,83,3.17,8.83,0.0449,0.125
	25	+10-Q,risk_factors,october,69216,0,32626,32626,36590,0,2891,4270,4270,2408,4.18,8.86,0.0617,0.1309
	26	+10-Q,unregistered_sales,backfilled_june,3344,0,2881,2881,463,0,222,284,284,83,6.64,7.71,0.0849,0.0986
	27	+10-Q,unregistered_sales,october,76866,0,58986,58986,17880,0,6818,9251,9222,2444,8.87,11.56,0.1204,0.1568
	28	+8-K,full_body,backfilled_june,103927,1,54360,54360,49567,0,15234,18326,18326,497,14.66,28.02,0.1763,0.3371
	29	+8-K,full_body,october,296674,0,150082,150082,146592,0,46414,56268,56240,2499,15.64,30.93,0.1897,0.3749

added results/ledger/gate_pass_by_form_run.csv +7 −0

		`@@ -0,0 +1,7 @@`
	1	+form_type,run,ledger_rows,lt30_words,gate_pass,calls,gate_rejected_zero,gate_fail_but_mentions,with_mentions,ledger_mentions,stored_mentions,issuers,productive_pct_ledger,productive_pct_calls,mentions_per_ledger_row,mentions_per_call
	2	+10-K,backfilled_june,27164,0,26336,26336,828,0,6204,15787,15787,497,22.84,23.56,0.5812,0.5994
	3	+10-K,october,116540,0,108974,108974,7566,0,24673,65835,65708,2101,21.17,22.64,0.5649,0.6041
	4	+10-Q,backfilled_june,23873,1069,14602,14590,9271,0,2969,5817,5817,84,12.44,20.35,0.2437,0.3987
	5	+10-Q,october,554920,0,349383,349383,205537,0,72736,146444,146187,2491,13.11,20.82,0.2639,0.4192
	6	+8-K,backfilled_june,103927,1,54360,54360,49567,0,15234,18326,18326,497,14.66,28.02,0.1763,0.3371
	7	+8-K,october,296674,0,150082,150082,146592,0,46414,56268,56240,2499,15.64,30.93,0.1897,0.3749

added results/ledger/gate_pass_by_run.csv +3 −0

		`@@ -0,0 +1,3 @@`
	1	+run,ledger_rows,lt30_words,gate_pass,calls,gate_rejected_zero,gate_fail_but_mentions,with_mentions,ledger_mentions,stored_mentions,issuers,productive_pct_ledger,productive_pct_calls,mentions_per_ledger_row,mentions_per_call
	2	+backfilled_june,154964,1070,95298,95286,59666,0,24407,39930,39930,500,15.75,25.61,0.2577,0.4191
	3	+october,968134,0,608439,608439,359695,0,143823,268547,268135,2499,14.86,23.64,0.2774,0.4414

added results/ledger/gate_pass_totals.json +27 −0

		`@@ -0,0 +1,27 @@`
	1	+{
	2	+ "ledger_rows": 1123098,
	3	+ "lt30_words": 1070,
	4	+ "gate_pass": 703737,
	5	+ "calls": 703725,
	6	+ "gate_rejected_zero": 419361,
	7	+ "gate_fail_but_mentions": 0,
	8	+ "with_mentions": 168230,
	9	+ "ledger_mentions": 308477,
	10	+ "stored_mentions": 308065,
	11	+ "issuers": 2499,
	12	+ "productive_pct_ledger": 14.98,
	13	+ "productive_pct_calls": 23.91,
	14	+ "mentions_per_ledger_row": 0.2747,
	15	+ "mentions_per_call": 0.4383,
	16	+ "usd_per_call_paper_total_775": 0.001274,
	17	+ "usd_per_october_row_paper_total_775": 0.000801,
	18	+ "usd_per_call_from_0_0008_x_ledger": 0.001277,
	19	+ "october_rows": 968134,
	20	+ "october_calls": 608439,
	21	+ "headline_before": {
	22	+ "sections_submitted": 1123098,
	23	+ "productive_pct": 15.0,
	24	+ "mentions_per_section": 0.27,
	25	+ "usd_per_section": 0.0008
	26	+ }
	27	+}
		\ No newline at end of file

added results/ledger/ledger_by_5min.csv +103 −0

		`@@ -0,0 +1,103 @@`
	1	+bin5,rows,with_mentions,mentions,zero_pct,calls,flushes
	2	+2026-10-27 21:35:00,4350,609,1000,86.0,2908,4350
	3	+2026-10-27 21:40:00,14971,2246,4123,85.0,9278,14971
	4	+2026-10-27 21:45:00,16678,2317,4178,86.11,10174,16678
	5	+2026-10-27 21:50:00,12278,1643,2951,86.62,7438,12278
	6	+2026-10-27 21:55:00,8128,1156,2092,85.78,4731,8128
	7	+2026-10-27 22:00:00,7568,1251,2436,83.47,4736,7568
	8	+2026-10-27 22:05:00,8226,1321,2794,83.94,5159,8226
	9	+2026-10-27 22:10:00,12652,1770,3371,86.01,7682,12652
	10	+2026-10-27 22:15:00,6291,1012,1741,83.91,3959,6291
	11	+2026-10-27 22:20:00,13126,1895,3228,85.56,8137,13126
	12	+2026-10-27 22:25:00,15316,2276,4063,85.14,9166,15316
	13	+2026-10-27 22:30:00,12444,1786,3301,85.65,7539,12444
	14	+2026-10-27 22:35:00,16234,2561,4495,84.22,9837,16234
	15	+2026-10-27 22:40:00,8706,1277,2272,85.33,5610,8706
	16	+2026-10-27 22:45:00,8695,1095,1908,87.41,5337,8695
	17	+2026-10-27 22:50:00,7557,1051,1827,86.09,4643,7557
	18	+2026-10-27 22:55:00,9490,1209,2076,87.26,5474,9490
	19	+2026-10-27 23:00:00,10337,1296,2126,87.46,5849,10337
	20	+2026-10-27 23:05:00,8797,1166,2072,86.75,5161,8797
	21	+2026-10-27 23:10:00,11704,1724,2917,85.27,6758,11704
	22	+2026-10-27 23:15:00,9736,1309,2468,86.56,5873,9736
	23	+2026-10-27 23:20:00,5649,889,1499,84.26,3657,5649
	24	+2026-10-27 23:25:00,9272,1466,2778,84.19,5531,9272
	25	+2026-10-27 23:30:00,6374,746,1503,88.3,3769,6374
	26	+2026-10-27 23:35:00,9361,1115,2027,88.09,5877,9361
	27	+2026-10-27 23:40:00,14467,2186,3883,84.89,9223,14467
	28	+2026-10-27 23:45:00,9476,1277,2326,86.52,5957,9476
	29	+2026-10-27 23:50:00,10573,1330,2314,87.42,6185,10573
	30	+2026-10-27 23:55:00,10762,1395,2621,87.04,7042,10762
	31	+2026-10-28 00:00:00,8266,1260,2608,84.76,5571,8266
	32	+2026-10-28 00:05:00,10799,1580,3074,85.37,7176,10799
	33	+2026-10-28 00:10:00,8422,1348,2507,83.99,5542,8422
	34	+2026-10-28 00:15:00,10262,1461,2786,85.76,6392,10262
	35	+2026-10-28 00:20:00,8922,1232,2647,86.19,5590,8922
	36	+2026-10-28 00:25:00,5251,897,1754,82.92,3301,5251
	37	+2026-10-28 00:30:00,5786,796,1500,86.24,3666,5786
	38	+2026-10-28 00:35:00,7134,1000,1923,85.98,4397,7134
	39	+2026-10-28 00:40:00,12516,1940,3607,84.5,8081,12516
	40	+2026-10-28 00:45:00,7679,1248,2318,83.75,4851,7679
	41	+2026-10-28 00:50:00,11588,1443,2612,87.55,7517,11588
	42	+2026-10-28 00:55:00,11258,1579,2985,85.97,7198,11258
	43	+2026-10-28 01:00:00,8677,1420,2841,83.63,5338,8677
	44	+2026-10-28 01:05:00,7428,1732,3209,76.68,4781,7428
	45	+2026-10-28 01:10:00,253,253,500,0.0,253,253
	46	+2026-10-28 01:20:00,245,245,500,0.0,245,245
	47	+2026-10-28 01:25:00,6,6,11,0.0,6,6
	48	+2026-10-28 06:40:00,10458,488,1034,95.33,5998,10458
	49	+2026-10-28 06:45:00,11183,1488,2933,86.69,6736,11183
	50	+2026-10-28 06:50:00,4578,563,1248,87.7,2767,4578
	51	+2026-10-28 06:55:00,8928,970,1877,89.14,5656,8928
	52	+2026-10-28 07:00:00,13065,1846,3544,85.87,8199,13065
	53	+2026-10-28 07:05:00,5495,737,1257,86.59,3531,5495
	54	+2026-10-28 07:10:00,13148,1932,3884,85.31,8222,13148
	55	+2026-10-28 07:15:00,14377,2207,4336,84.65,9039,14377
	56	+2026-10-28 07:20:00,14323,2356,4280,83.55,9481,14323
	57	+2026-10-28 07:25:00,15262,2242,4034,85.31,9867,15262
	58	+2026-10-28 07:30:00,13643,1950,3661,85.71,8627,13643
	59	+2026-10-28 07:35:00,11317,1918,3845,83.05,6959,11317
	60	+2026-10-28 07:40:00,12049,2131,4081,82.31,7761,12049
	61	+2026-10-28 07:45:00,14266,1947,3575,86.35,8570,14266
	62	+2026-10-28 07:50:00,13271,1874,3360,85.88,8687,13271
	63	+2026-10-28 07:55:00,8061,1150,2030,85.73,5247,8061
	64	+2026-10-28 08:00:00,8671,1273,2519,85.32,5375,8671
	65	+2026-10-28 08:05:00,6292,971,1807,84.57,4000,6292
	66	+2026-10-28 08:10:00,13917,2218,4325,84.06,8464,13917
	67	+2026-10-28 08:15:00,10201,1720,3341,83.14,6351,10201
	68	+2026-10-28 08:20:00,12910,1818,3468,85.92,8165,12910
	69	+2026-10-28 08:25:00,12030,1728,3307,85.64,7847,12030
	70	+2026-10-28 08:30:00,11092,1597,3062,85.6,7157,11092
	71	+2026-10-28 08:35:00,9716,1161,2001,88.05,6025,9716
	72	+2026-10-28 08:40:00,8927,1327,2590,85.13,5581,8927
	73	+2026-10-28 08:45:00,5340,774,1703,85.51,3383,5340
	74	+2026-10-28 08:50:00,8143,1123,2185,86.21,5043,8143
	75	+2026-10-28 08:55:00,7749,1124,2002,85.49,4697,7749
	76	+2026-10-28 09:00:00,12882,1830,3218,85.79,8257,12882
	77	+2026-10-28 09:05:00,11277,1510,2855,86.61,7136,11277
	78	+2026-10-28 09:10:00,9890,1466,2742,85.18,6635,9890
	79	+2026-10-28 09:15:00,8843,1304,2341,85.25,5743,8843
	80	+2026-10-28 09:20:00,13311,2171,4295,83.69,8526,13311
	81	+2026-10-28 09:25:00,10741,1668,3076,84.47,7093,10741
	82	+2026-10-28 09:30:00,13081,1893,3691,85.53,8399,13081
	83	+2026-10-28 09:35:00,8869,1402,2517,84.19,6010,8869
	84	+2026-10-28 09:40:00,10958,1661,3082,84.84,7112,10958
	85	+2026-10-28 09:45:00,7764,1113,2146,85.66,5017,7764
	86	+2026-10-28 09:50:00,12713,1842,3387,85.51,8209,12713
	87	+2026-10-28 09:55:00,10382,1610,3116,84.49,6761,10382
	88	+2026-10-28 10:00:00,8939,1599,3090,82.11,5860,8939
	89	+2026-10-28 10:05:00,9496,1498,2900,84.22,5904,9496
	90	+2026-10-28 10:10:00,5551,942,2028,83.03,3609,5551
	91	+2026-10-28 10:15:00,6155,1042,1939,83.07,3883,6155
	92	+2026-10-28 10:20:00,10393,1756,3037,83.1,7032,10393
	93	+2026-10-28 10:25:00,10744,1686,3266,84.31,6518,10744
	94	+2026-10-28 10:30:00,5861,970,1825,83.45,3687,5861
	95	+2026-10-28 10:35:00,10877,1896,3671,82.57,6864,10877
	96	+2026-10-28 10:40:00,7599,1184,2043,84.42,4755,7599
	97	+2026-10-28 10:45:00,7156,1365,2806,80.93,4518,7156
	98	+2026-10-28 10:50:00,4957,918,1616,81.48,3274,4957
	99	+2026-10-28 10:55:00,5525,905,1696,83.62,3602,5525
	100	+2026-10-28 11:00:00,7816,1336,2302,82.91,5008,7816
	101	+2026-10-28 11:05:00,5886,1129,1990,80.82,3669,5886
	102	+2026-10-28 11:10:00,6677,1369,2298,79.5,4261,6677
	103	+2026-10-28 11:15:00,1669,311,513,81.37,1067,1669

added results/ledger/ledger_by_minute.csv +443 −0

		`@@ -0,0 +1,443 @@`
	1	+bin1,rows,with_mentions,mentions,zero_pct,calls,zero_pct_among_calls
	2	+2026-10-27 21:35:00,7,0,0,100.0,6,100.0
	3	+2026-10-27 21:38:00,2144,296,500,86.19,1469,79.85
	4	+2026-10-27 21:39:00,2199,313,500,85.77,1433,78.16
	5	+2026-10-27 21:40:00,4187,467,855,88.85,2617,82.16
	6	+2026-10-27 21:41:00,1657,303,548,81.71,1026,70.47
	7	+2026-10-27 21:42:00,2874,536,1003,81.35,1800,70.22
	8	+2026-10-27 21:43:00,3524,527,1001,85.05,2171,75.73
	9	+2026-10-27 21:44:00,2729,413,716,84.87,1664,75.18
	10	+2026-10-27 21:45:00,5114,616,1003,87.95,3133,80.34
	11	+2026-10-27 21:46:00,2462,363,613,85.26,1514,76.02
	12	+2026-10-27 21:47:00,3715,488,1001,86.86,2245,78.26
	13	+2026-10-27 21:48:00,3411,540,1002,84.17,2115,74.47
	14	+2026-10-27 21:49:00,1976,310,559,84.31,1167,73.44
	15	+2026-10-27 21:50:00,2235,270,501,87.92,1368,80.26
	16	+2026-10-27 21:51:00,1943,275,503,85.85,1227,77.59
	17	+2026-10-27 21:52:00,4230,582,947,86.24,2727,78.66
	18	+2026-10-27 21:53:00,2082,265,500,87.27,1150,76.96
	19	+2026-10-27 21:54:00,1788,251,500,85.96,966,74.02
	20	+2026-10-27 21:56:00,2270,284,501,87.49,1322,78.52
	21	+2026-10-27 21:57:00,2641,286,502,89.17,1469,80.53
	22	+2026-10-27 21:58:00,203,55,86,72.91,132,58.33
	23	+2026-10-27 21:59:00,3014,531,1003,82.38,1808,70.63
	24	+2026-10-27 22:00:00,1548,229,503,85.21,964,76.24
	25	+2026-10-27 22:01:00,1763,268,500,84.8,1134,76.37
	26	+2026-10-27 22:02:00,1455,271,500,81.37,895,69.72
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added results/ledger/ledger_collisions.csv +305 −0

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added results/ledger/ledger_collisions_by_form.csv +4 −0

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added results/ledger/log_failures_by_hour.csv +17 −0

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added results/ledger/log_summary.json +1021 −0

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added results/ledger/manifest.json +49 −0

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added results/ledger/resume_batch1_vs_expected.csv +14 −0

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added results/ledger/table3_ledger_vs_stored.csv +4 −0

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added results/ledger/throughput.json +105 −0

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spb/firc0001_uqo

N1: ledger reconciliation (gate pass / true call count, backfilled block, outage, collisions, logs, throughput)

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