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N5: focus-window coverage of every ledger section (shown fraction, last offset, gate hits shown, plateau, short MD&As)

Items: FIRC0001-EDIT-08 THEO-09 AI_C-06 ECON-05 AI_C-20 DATA-08 THEO-13.
_focus replicated span-exactly on 1,123,098 sections (0 mismatches; 23.4 GB streamed in 585 s); Python gate = DuckDB gate = 703,737.
10-K MD&A: median 9,905 words / 64,194 chars reproduced; median shown 9.3 %, last shown offset at 16 % of the text, 90.5 % of sections shown < 25 %;
62.7 % of all mentions come from sections shown < 25 %; 0 messages cut at 14,000 chars; mentions/section plateau ~1.5 beyond 20k chars.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent 906fea1 Browse files

10 changed files +446 −0

10 changed files
Unified Split
added code/audit/a05_focus_coverage.py +165 −0
@@ -0,0 +1,165 @@
1 +"""N5 - focus-window coverage: what share of every ledger section the model actually saw (cluster E).
2 +
3 +Imports _focus / _PROJECT_GATE from the hash-verified snapshot (pure functions). For each of the
4 +1,123,098 ledger sections: length, Python gate pass, gate hits, shown characters (replicated span
5 +logic, asserted equal to the real _focus output), offset of the last shown character, hits shown,
6 +14,000-character message cut, capex vocabulary inside/outside the shown windows. Aggregates by form x
7 +item; n_mentions vs length (plateau); <30-word / incorporated-by-reference 10-K MD&As by year;
8 +median words of ledger 10-K MD&A (the 9,905 figure). Read-only; per-section features kept on the node.
9 +"""
10 +from __future__ import annotations
11 +import json, re, sys, time
12 +from pathlib import Path
13 +import multiprocessing as mp
14 +sys.path.insert(0, str(Path(__file__).resolve().parent))
15 +from common import *
16 +sys.path.insert(0, str(Path(__file__).resolve().parent))
17 +from spid.extract.llm import _focus # snapshot copy
18 +from spid.extract.rules import _PROJECT_GATE # snapshot copy
19 +
20 +BUDGET = 9000
21 +CAPEX = re.compile(r"capital expenditure|liquidity and capital resources|commitment", re.I)
22 +
23 +def spans_like_focus(text: str, hits):
24 + """Replicates _focus (llm.py:55-80) and returns the chunks [(a,b)] actually emitted."""
25 + L = len(text)
26 + if L <= BUDGET:
27 + return [(0, L)], True
28 + spans = []
29 + for s, e in hits:
30 + spans.append((max(0, s - 220), min(L, e + 380)))
31 + if sum(b - a for a, b in spans) > BUDGET * 1.4:
32 + break
33 + if not spans:
34 + return [(0, BUDGET)], True
35 + spans.sort()
36 + merged = [spans[0]]
37 + for a, b in spans[1:]:
38 + if a <= merged[-1][1]:
39 + merged[-1] = (merged[-1][0], max(merged[-1][1], b))
40 + else:
41 + merged.append((a, b))
42 + chunks, total = [], 0
43 + for a, b in merged:
44 + chunks.append((a, b)); total += b - a
45 + if total >= BUDGET:
46 + break
47 + return chunks, False
48 +
49 +def feat(item):
50 + sid, text = item
51 + L = len(text)
52 + hits = [(m.start(), m.end()) for m in _PROJECT_GATE.finditer(text)]
53 + nh = len(hits)
54 + chunks, whole = spans_like_focus(text, hits)
55 + shown = sum(b - a for a, b in chunks)
56 + last = chunks[-1][1]
57 + focus = _focus(text)
58 + eq = ("\n...\n".join(text[a:b] for a, b in chunks) == focus)
59 + # hits inside the chunks
60 + hs = 0; ci = 0
61 + for s, _ in hits:
62 + while ci < len(chunks) and s >= chunks[ci][1]:
63 + ci += 1
64 + if ci < len(chunks) and chunks[ci][0] <= s < chunks[ci][1]:
65 + hs += 1
66 + cap_all = 0; cap_in = 0
67 + if L > BUDGET:
68 + for m in CAPEX.finditer(text):
69 + cap_all += 1
70 + s = m.start()
71 + if any(a <= s < b for a, b in chunks):
72 + cap_in += 1
73 + return (sid, L, nh > 0, nh, shown, last, hs, len(focus), eq, cap_all, cap_in, len(chunks))
74 +
75 +def main():
76 + r = Run("focus")
77 + con = connect(SPID_DB)
78 + con.execute("CREATE TEMP TABLE led AS SELECT section_id FROM extracted_sections")
79 + q = """SELECT s.section_id, s.text FROM src_sections s JOIN led USING (section_id)
80 + UNION ALL SELECT s.section_id, s.text FROM spid_sections s JOIN led USING (section_id)"""
81 + cur = con.execute(q)
82 + rows_out = []; t0 = time.time(); done = 0; chars = 0
83 + ctx = mp.get_context("fork")
84 + with ctx.Pool(4) as pool:
85 + while True:
86 + rows = cur.fetchmany(4000)
87 + if not rows:
88 + break
89 + chars += sum(len(t) for _, t in rows)
90 + rows_out.extend(pool.imap_unordered(feat, rows, chunksize=100))
91 + done += len(rows)
92 + if done % 100000 < 4000:
93 + print(f" {done} sections, {chars/1e9:.2f} GB, {time.time()-t0:.0f}s", flush=True)
94 + r.notes["sections_streamed"] = done; r.notes["chars_streamed"] = chars
95 + import pyarrow as pa, pyarrow.parquet as pq
96 + names = ["section_id", "nchar", "gate_py", "n_hits", "shown_chars", "last_offset", "hits_shown", "focus_len", "replica_eq", "capex_all", "capex_shown", "n_chunks"]
97 + tbl = pa.table({n: [row[i] for row in rows_out] for i, n in enumerate(names)})
98 + pq.write_table(tbl, r.out / "section_features.parquet") # stays on the node
99 + con.register("ft", tbl)
100 + tmin = con.execute("select min(extracted_at) from extracted_sections").fetchone()[0]
101 + con.execute(f"""CREATE TEMP TABLE F AS
102 + SELECT ft.*, e.n_mentions, CASE WHEN e.extracted_at = TIMESTAMP '{tmin}' THEN 'backfilled_june' ELSE 'october' END run,
103 + m.section_name item, m.word_count, f.form_type form, year(f.filing_date) yr,
104 + shown_chars*1.0/nchar AS shown_frac, last_offset*1.0/nchar AS last_frac,
105 + CASE WHEN n_hits > 0 THEN hits_shown*1.0/n_hits END AS hits_frac,
106 + (9 + length(m.section_name) + 2 + focus_len) > 14000 AS msg_truncated,
107 + nchar > {BUDGET} AS over_budget
108 + FROM ft JOIN extracted_sections e USING (section_id)
109 + JOIN (SELECT section_id, section_name, word_count FROM src_sections UNION ALL SELECT section_id, section_name, word_count FROM spid_sections) m USING (section_id)
110 + JOIN src_filings f ON f.accession_number = e.accession_number""")
111 + r.notes["replica_mismatches"] = con.execute("select count(*) from F where not replica_eq").fetchone()[0]
112 + r.notes["gate_py_pass"] = con.execute("select count(*) from F where gate_py").fetchone()[0]
113 + r.notes["gate_py_pass_by_form"] = con.execute("select form, count(*) filter (where gate_py), count(*) from F group by 1 order by 1").fetchall()
114 + r.notes["gate_fail_with_mentions"] = con.execute("select count(*) from F where not gate_py and n_mentions>0").fetchone()[0]
115 + agg = """count(*) n, count(*) FILTER (WHERE gate_py) gate_pass, count(*) FILTER (WHERE over_budget) over_9000_chars, round(100.0*count(*) FILTER (WHERE over_budget)/count(*),2) pct_over_9000,
116 + round(quantile_cont(nchar,0.1)) chars_p10, round(quantile_cont(nchar,0.5)) chars_p50, round(quantile_cont(nchar,0.9)) chars_p90,
117 + round(quantile_cont(shown_frac,0.1),3) shown_p10, round(quantile_cont(shown_frac,0.5),3) shown_p50, round(quantile_cont(shown_frac,0.9),3) shown_p90, round(avg(shown_frac),3) shown_mean,
118 + round(quantile_cont(shown_frac,0.5) FILTER (WHERE over_budget),3) shown_p50_over_budget,
119 + round(quantile_cont(last_frac,0.1),3) last_p10, round(quantile_cont(last_frac,0.5),3) last_p50, round(quantile_cont(last_frac,0.9),3) last_p90,
120 + round(100.0*count(*) FILTER (WHERE shown_frac < 0.25)/count(*),2) pct_shown_lt_25, round(100.0*count(*) FILTER (WHERE last_frac < 0.5)/count(*),2) pct_last_lt_50,
121 + round(100.0*count(*) FILTER (WHERE msg_truncated)/count(*),2) pct_msg_truncated_14k, round(avg(hits_frac),3) hits_shown_mean, round(quantile_cont(hits_frac,0.5),3) hits_shown_p50,
122 + sum(n_mentions) mentions, sum(n_mentions) FILTER (WHERE shown_frac < 0.25) mentions_from_lt25, round(100.0*sum(n_mentions) FILTER (WHERE shown_frac < 0.25)/nullif(sum(n_mentions),0),2) pct_mentions_from_lt25,
123 + round(100.0*sum(capex_shown)/nullif(sum(capex_all),0),2) pct_capex_vocab_shown_over_budget"""
124 + r.dump("focus_coverage_by_form_item.csv", con, f"SELECT form, item, {agg} FROM F GROUP BY 1,2 ORDER BY 1,2")
125 + r.dump("focus_coverage_by_form.csv", con, f"SELECT form, {agg} FROM F GROUP BY 1 ORDER BY 1")
126 + r.dump("focus_coverage_by_form_item_run.csv", con, f"SELECT form, item, run, {agg} FROM F GROUP BY 1,2,3 ORDER BY 1,2,3")
127 + r.dump("focus_coverage_calls_only_by_form_item.csv", con, f"SELECT form, item, {agg} FROM F WHERE gate_py AND word_count >= 30 GROUP BY 1,2 ORDER BY 1,2")
128 + r.dump("mentions_vs_length_by_item.csv", con, """
129 + SELECT form, item, CASE WHEN nchar<3000 THEN 'a<3k' WHEN nchar<6000 THEN 'b3-6k' WHEN nchar<9000 THEN 'c6-9k' WHEN nchar<12000 THEN 'd9-12k' WHEN nchar<20000 THEN 'e12-20k' WHEN nchar<40000 THEN 'f20-40k' WHEN nchar<80000 THEN 'g40-80k' ELSE 'h>=80k' END len_bin,
130 + count(*) n, count(*) FILTER (WHERE gate_py) calls, round(avg(n_mentions),4) mean_mentions, round(avg(n_mentions) FILTER (WHERE gate_py),4) mean_mentions_per_call,
131 + round(100.0*count(*) FILTER (WHERE n_mentions>0)/count(*),2) productive_pct, round(avg(shown_frac),3) shown_mean, round(avg(shown_chars)) shown_chars_mean,
132 + round(1e4*sum(n_mentions)/nullif(sum(shown_chars),0),4) mentions_per_10k_shown_chars
133 + FROM F GROUP BY 1,2,3 ORDER BY 1,2,3""")
134 + r.dump("shown_fraction_bins.csv", con, """
135 + SELECT form, CASE WHEN shown_frac >= 0.9 THEN 'a>=0.9' WHEN shown_frac >= 0.25 THEN 'b0.25-0.9' ELSE 'c<0.25' END bin, count(*) n, count(*) FILTER (WHERE gate_py AND word_count>=30) calls,
136 + count(*) FILTER (WHERE n_mentions>0) with_mentions, sum(n_mentions) mentions FROM F GROUP BY 1,2 ORDER BY 1,2""")
137 + # 10-K MD&A coverage hole by year: all parsed + legacy 10-K md_and_a sections (ledgered or not)
138 + r.dump("mdna_10k_short_by_year.csv", con, """
139 + WITH a AS (SELECT s.section_id, s.word_count, year(f.filing_date) yr, s.section_id IN (SELECT section_id FROM extracted_sections) ledgered,
140 + regexp_matches(left(s.text, 3000), 'incorporated .{0,40}by reference', 'i') incorp_head
141 + FROM src_sections s JOIN src_filings f USING (accession_number) WHERE f.form_type='10-K' AND s.section_name='md_and_a' AND s.accession_number NOT IN (SELECT accession_number FROM spid_sections)
142 + UNION ALL
143 + SELECT s.section_id, s.word_count, year(f.filing_date), TRUE, regexp_matches(left(s.text, 3000), 'incorporated .{0,40}by reference', 'i')
144 + FROM spid_sections s JOIN src_filings f USING (accession_number) WHERE s.section_name='md_and_a')
145 + SELECT yr, count(*) mdna_sections, count(*) FILTER (WHERE word_count < 30) lt30_words, round(100.0*count(*) FILTER (WHERE word_count < 30)/count(*),2) pct_lt30,
146 + count(*) FILTER (WHERE word_count < 300 AND incorp_head) lt300_incorporated_by_ref, count(*) FILTER (WHERE NOT ledgered) not_ledgered, round(median(word_count)) median_words
147 + FROM a GROUP BY 1 ORDER BY 1""")
148 + K = {}
149 + K["ledger_10k_mdna_median_words"] = con.execute("select median(word_count) from F where form='10-K' and item='md_and_a'").fetchone()[0]
150 + K["ledger_10k_mdna_median_chars"] = con.execute("select median(nchar) from F where form='10-K' and item='md_and_a'").fetchone()[0]
151 + K["ledger_10k_mdna_n"] = con.execute("select count(*) from F where form='10-K' and item='md_and_a'").fetchone()[0]
152 + K["ledger_10k_mdna_shown_p50"] = con.execute("select median(shown_frac) from F where form='10-K' and item='md_and_a'").fetchone()[0]
153 + K["ledger_10k_mdna_last_p50"] = con.execute("select median(last_frac) from F where form='10-K' and item='md_and_a'").fetchone()[0]
154 + K["ledger_10k_mdna_pct_shown_lt_25"] = con.execute("select 100.0*count(*) filter (where shown_frac<0.25)/count(*) from F where form='10-K' and item='md_and_a'").fetchone()[0]
155 + K["ledger_sections_over_9000_chars"], K["ledger_sections"] = con.execute("select count(*) filter (where over_budget), count(*) from F").fetchone()
156 + K["calls_over_9000_chars"], K["calls"] = con.execute("select count(*) filter (where over_budget), count(*) from F where gate_py and word_count>=30").fetchone()
157 + K["mentions_from_sections_shown_lt_25"], K["mentions_total"] = con.execute("select sum(n_mentions) filter (where shown_frac<0.25), sum(n_mentions) from F").fetchone()
158 + K["msg_truncated_14k"] = con.execute("select count(*) from F where msg_truncated").fetchone()[0]
159 + K["gate_py_pass_total"] = r.notes["gate_py_pass"]; K["gate_duckdb_pass_total_N1"] = 703737
160 + K["mean_shown_frac_over_budget"] = con.execute("select avg(shown_frac) from F where over_budget").fetchone()[0]
161 + r.write_json("stats_keys.json", K)
162 + r.finish()
163 +
164 +if __name__ == "__main__":
165 + main()
added results/focus/focus_coverage_by_form.csv +4 −0
@@ -0,0 +1,4 @@
1 +form,n,gate_pass,over_9000_chars,pct_over_9000,chars_p10,chars_p50,chars_p90,shown_p10,shown_p50,shown_p90,shown_mean,shown_p50_over_budget,last_p10,last_p50,last_p90,pct_shown_lt_25,pct_last_lt_50,pct_msg_truncated_14k,hits_shown_mean,hits_shown_p50,mentions,mentions_from_lt25,pct_mentions_from_lt25,pct_capex_vocab_shown_over_budget
2 +10-K,143704,135310,85179,59.27,924.0,28255.0,110017.0,0.051,0.217,1.0,0.487,0.102,0.078,0.443,1.0,52.12,51.66,0.0,0.53,0.338,81622,68572,84.01,7.44
3 +10-Q,578793,363985,205571,35.52,335.0,3503.0,78237.0,0.08,1.0,1.0,0.695,0.115,0.147,1.0,1.0,31.4,28.96,0.0,0.61,0.656,152261,120560,79.18,9.02
4 +8-K,400601,204442,35345,8.82,2538.0,3816.0,8528.0,1.0,1.0,1.0,0.94,0.304,1.0,1.0,1.0,3.32,1.14,0.0,0.984,1.0,74594,4284,5.74,51.49
added results/focus/focus_coverage_by_form_item.csv +16 −0
@@ -0,0 +1,16 @@
1 +form,item,n,gate_pass,over_9000_chars,pct_over_9000,chars_p10,chars_p50,chars_p90,shown_p10,shown_p50,shown_p90,shown_mean,shown_p50_over_budget,last_p10,last_p50,last_p90,pct_shown_lt_25,pct_last_lt_50,pct_msg_truncated_14k,hits_shown_mean,hits_shown_p50,mentions,mentions_from_lt25,pct_mentions_from_lt25,pct_capex_vocab_shown_over_budget
2 +10-K,business,29285,29118,23748,81.09,5587.0,37168.0,100505.0,0.053,0.157,1.0,0.32,0.127,0.076,0.259,1.0,66.62,68.33,0.0,0.423,0.261,30492,26256,86.11,10.03
3 +10-K,full_body,1230,1229,1229,99.92,165349.0,304265.0,517545.0,0.011,0.021,0.043,0.027,0.021,0.028,0.064,0.186,99.84,98.94,0.0,0.056,0.041,824,824,100.0,1.72
4 +10-K,md_and_a,28919,28050,27175,93.97,26935.0,64194.0,123299.0,0.043,0.093,0.24,0.159,0.089,0.063,0.162,0.573,90.5,87.88,0.0,0.214,0.156,39099,37824,96.74,5.79
5 +10-K,properties,28236,25940,1592,5.64,491.0,1255.0,4866.0,1.0,1.0,1.0,0.957,0.217,1.0,1.0,1.0,3.21,2.66,0.0,0.975,1.0,7879,891,11.31,14.03
6 +10-K,quantitative_disclosures,26876,22032,3208,11.94,638.0,3354.0,9995.0,0.449,1.0,1.0,0.912,0.28,0.97,1.0,1.0,5.38,4.96,0.0,0.947,1.0,531,176,33.15,8.5
7 +10-K,risk_factors,29158,28941,28227,96.81,22943.0,62542.0,156060.0,0.038,0.101,0.277,0.151,0.098,0.051,0.177,0.578,87.93,87.52,0.0,0.25,0.176,2797,2601,92.99,11.49
8 +10-Q,controls_and_procedures,92393,20913,1727,1.87,985.0,1710.0,3574.0,1.0,1.0,1.0,0.986,0.162,1.0,1.0,1.0,1.05,0.97,0.0,0.961,1.0,7082,227,3.21,5.81
9 +10-Q,financial_statements,91882,90106,88971,96.83,31004.0,64457.0,123191.0,0.053,0.106,0.225,0.146,0.104,0.097,0.239,0.654,92.4,83.74,0.0,0.307,0.234,30159,28422,94.24,9.69
10 +10-Q,full_document,241,241,241,100.0,118565.0,348536.0,823174.0,0.008,0.019,0.068,0.03,0.019,0.026,0.143,0.347,100.0,100.0,0.0,0.061,0.046,107,107,100.0,2.48
11 +10-Q,legal_proceedings,66998,20043,2219,3.31,232.0,522.0,3847.0,1.0,1.0,1.0,0.976,0.25,1.0,1.0,1.0,1.66,0.87,0.0,0.98,1.0,845,92,10.89,15.22
12 +10-Q,md_and_a,91371,90269,86279,94.43,21446.0,47488.0,95664.0,0.057,0.132,0.309,0.192,0.126,0.086,0.24,0.731,84.69,79.65,0.0,0.28,0.211,99305,89215,89.84,8.26
13 +10-Q,quantitative_disclosures,83138,46720,4039,4.86,251.0,1510.0,6250.0,1.0,1.0,1.0,0.969,0.36,1.0,1.0,1.0,1.04,0.73,0.0,0.986,1.0,808,62,7.67,30.96
14 +10-Q,risk_factors,72560,33826,16590,22.86,197.0,969.0,93477.0,0.067,1.0,1.0,0.807,0.078,0.11,1.0,1.0,17.69,17.8,0.0,0.664,1.0,4420,1894,42.85,10.53
15 +10-Q,unregistered_sales,80210,61867,5505,6.86,2253.0,3886.0,7603.0,1.0,1.0,1.0,0.948,0.193,1.0,1.0,1.0,4.28,3.28,0.0,0.984,1.0,9535,541,5.67,11.08
16 +8-K,full_body,400601,204442,35345,8.82,2538.0,3816.0,8528.0,1.0,1.0,1.0,0.94,0.304,1.0,1.0,1.0,3.32,1.14,0.0,0.984,1.0,74594,4284,5.74,51.49
added results/focus/focus_coverage_by_form_item_run.csv +29 −0
@@ -0,0 +1,29 @@
1 +form,item,run,n,gate_pass,over_9000_chars,pct_over_9000,chars_p10,chars_p50,chars_p90,shown_p10,shown_p50,shown_p90,shown_mean,shown_p50_over_budget,last_p10,last_p50,last_p90,pct_shown_lt_25,pct_last_lt_50,pct_msg_truncated_14k,hits_shown_mean,hits_shown_p50,mentions,mentions_from_lt25,pct_mentions_from_lt25,pct_capex_vocab_shown_over_budget
2 +10-K,business,backfilled_june,5545,5542,5257,94.81,14938.0,40344.0,92815.0,0.056,0.146,0.403,0.213,0.138,0.079,0.227,0.81,75.62,78.83,0.0,0.326,0.226,5934,4752,80.08,9.15
3 +10-K,business,october,23740,23576,18491,77.89,5212.0,36319.0,102479.0,0.053,0.16,1.0,0.346,0.124,0.075,0.268,1.0,64.52,65.88,0.0,0.445,0.272,24558,21504,87.56,10.38
4 +10-K,full_body,backfilled_june,1230,1229,1229,99.92,165349.0,304265.0,517545.0,0.011,0.021,0.043,0.027,0.021,0.028,0.064,0.186,99.84,98.94,0.0,0.056,0.041,824,824,100.0,1.72
5 +10-K,md_and_a,backfilled_june,5198,5096,4927,94.79,30305.0,65644.0,135048.0,0.039,0.088,0.214,0.146,0.085,0.056,0.149,0.487,92.32,90.4,0.0,0.195,0.141,6703,6487,96.78,5.8
6 +10-K,md_and_a,october,23721,22954,22248,93.79,25898.0,63899.0,120928.0,0.044,0.094,0.248,0.162,0.09,0.065,0.165,0.594,90.11,87.32,0.0,0.218,0.16,32396,31337,96.73,5.78
7 +10-K,properties,backfilled_june,5084,4897,216,4.25,608.0,1268.0,4547.0,1.0,1.0,1.0,0.965,0.125,1.0,1.0,1.0,2.97,3.07,0.0,0.971,1.0,1721,123,7.15,6.85
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added results/focus/manifest.json +58 −0
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added results/focus/mdna_10k_short_by_year.csv +18 −0
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1 +yr,mdna_sections,lt30_words,pct_lt30,lt300_incorporated_by_ref,not_ledgered,median_words
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added results/focus/mentions_vs_length_by_item.csv +112 −0
@@ -0,0 +1,112 @@
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49 +10-Q,controls_and_procedures,c6-9k,1667,1562,0.1746,0.1863,10.5,1.0,7207.0,0.2422
50 +10-Q,controls_and_procedures,d9-12k,540,516,0.187,0.1957,11.67,0.457,4718.0,0.3964
51 +10-Q,controls_and_procedures,e12-20k,300,268,0.6033,0.6754,18.0,0.367,5439.0,1.1092
52 +10-Q,controls_and_procedures,f20-40k,84,83,0.2143,0.2169,14.29,0.2,5176.0,0.414
53 +10-Q,controls_and_procedures,g40-80k,175,175,0.3829,0.3829,30.29,0.115,6940.0,0.5517
54 +10-Q,controls_and_procedures,h>=80k,628,628,0.2213,0.2213,15.29,0.048,6806.0,0.3252
55 +10-Q,financial_statements,a<3k,2293,555,0.0279,0.1153,1.61,1.0,869.0,0.3211
56 +10-Q,financial_statements,b3-6k,403,368,0.2258,0.2473,11.91,1.0,4313.0,0.5236
57 +10-Q,financial_statements,c6-9k,215,212,0.3442,0.3491,19.07,1.0,7290.0,0.4721
58 +10-Q,financial_statements,d9-12k,228,228,0.2412,0.2412,15.35,0.399,4167.0,0.5788
59 +10-Q,financial_statements,e12-20k,1102,1102,0.4156,0.4156,24.41,0.339,5608.0,0.7411
60 +10-Q,financial_statements,f20-40k,12758,12758,0.4176,0.4176,26.98,0.216,6776.0,0.6163
61 +10-Q,financial_statements,g40-80k,44583,44583,0.3653,0.3653,22.95,0.121,6922.0,0.5277
62 +10-Q,financial_statements,h>=80k,30300,30300,0.2576,0.2576,16.19,0.063,6899.0,0.3733
63 +10-Q,full_document,g40-80k,8,8,1.375,1.375,75.0,0.106,6613.0,2.0794
64 +10-Q,full_document,h>=80k,233,233,0.412,0.412,13.73,0.028,6715.0,0.6136
65 +10-Q,legal_proceedings,a<3k,58328,12572,0.005,0.023,0.45,1.0,697.0,0.0711
66 +10-Q,legal_proceedings,b3-6k,4790,3799,0.0436,0.055,3.09,1.0,4215.0,0.1035
67 +10-Q,legal_proceedings,c6-9k,1660,1510,0.0705,0.0775,4.7,1.0,7300.0,0.0965
68 +10-Q,legal_proceedings,d9-12k,798,764,0.084,0.0877,6.39,0.324,3352.0,0.2505
69 +10-Q,legal_proceedings,e12-20k,759,735,0.0883,0.0912,4.74,0.302,4505.0,0.1959
70 +10-Q,legal_proceedings,f20-40k,328,328,0.122,0.122,7.93,0.25,6501.0,0.1876
71 +10-Q,legal_proceedings,g40-80k,108,108,0.0926,0.0926,8.33,0.116,6063.0,0.1527
72 +10-Q,legal_proceedings,h>=80k,227,227,0.2026,0.2026,12.78,0.052,6894.0,0.2939
73 +10-Q,md_and_a,a<3k,3162,2149,0.0927,0.1363,7.37,1.0,1353.0,0.6848
74 +10-Q,md_and_a,b3-6k,1474,1405,0.2252,0.2363,12.96,1.0,4255.0,0.5294
75 +10-Q,md_and_a,c6-9k,456,436,0.4781,0.5,26.54,1.0,7363.0,0.6493
76 +10-Q,md_and_a,d9-12k,406,406,0.6872,0.6872,30.05,0.491,5144.0,1.3358
77 +10-Q,md_and_a,e12-20k,2696,2696,1.079,1.079,46.88,0.364,6061.0,1.7803
78 +10-Q,md_and_a,f20-40k,25258,25258,1.3259,1.3259,51.16,0.21,6401.0,2.0716
79 +10-Q,md_and_a,g40-80k,43048,43048,1.1532,1.1532,45.35,0.118,6330.0,1.8219
80 +10-Q,md_and_a,h>=80k,14871,14871,0.8165,0.8165,36.41,0.058,6250.0,1.3064
81 +10-Q,quantitative_disclosures,a<3k,57596,22443,0.004,0.0104,0.36,1.0,1009.0,0.0401
82 +10-Q,quantitative_disclosures,b3-6k,16617,15452,0.0205,0.0221,1.46,1.0,4204.0,0.0488
83 +10-Q,quantitative_disclosures,c6-9k,4886,4801,0.0244,0.0248,1.58,1.0,7245.0,0.0336
84 +10-Q,quantitative_disclosures,d9-12k,1866,1857,0.0139,0.014,1.18,0.392,4006.0,0.0348
85 +10-Q,quantitative_disclosures,e12-20k,1513,1507,0.0145,0.0146,0.73,0.37,5552.0,0.0262
86 +10-Q,quantitative_disclosures,f20-40k,527,527,0.0266,0.0266,1.52,0.277,6953.0,0.0382
87 +10-Q,quantitative_disclosures,g40-80k,66,66,0.3939,0.3939,15.15,0.124,6607.0,0.5963
88 +10-Q,quantitative_disclosures,h>=80k,67,67,0.403,0.403,19.4,0.053,7043.0,0.5722
89 +10-Q,risk_factors,a<3k,45276,8173,0.0106,0.0586,0.96,1.0,703.0,0.1504
90 +10-Q,risk_factors,b3-6k,7464,6079,0.1101,0.1352,9.11,1.0,4262.0,0.2584
91 +10-Q,risk_factors,c6-9k,3229,3068,0.1963,0.2066,14.21,1.0,7322.0,0.2682
92 +10-Q,risk_factors,d9-12k,1800,1750,0.1517,0.156,12.72,0.44,4562.0,0.3324
93 +10-Q,risk_factors,e12-20k,1908,1886,0.173,0.175,14.52,0.373,5563.0,0.3109
94 +10-Q,risk_factors,f20-40k,1526,1514,0.1664,0.1678,11.53,0.233,6452.0,0.258
95 +10-Q,risk_factors,g40-80k,3007,3006,0.1217,0.1218,7.15,0.106,6274.0,0.194
96 +10-Q,risk_factors,h>=80k,8350,8350,0.1511,0.1511,6.29,0.046,6452.0,0.2343
97 +10-Q,unregistered_sales,a<3k,21342,10524,0.0209,0.0424,1.97,1.0,2333.0,0.0896
98 +10-Q,unregistered_sales,b3-6k,44577,37930,0.1305,0.1533,10.24,1.0,4186.0,0.3117
99 +10-Q,unregistered_sales,c6-9k,8785,8176,0.266,0.2858,17.3,1.0,7117.0,0.3738
100 +10-Q,unregistered_sales,d9-12k,2259,2096,0.1284,0.1384,7.97,0.315,3211.0,0.3998
101 +10-Q,unregistered_sales,e12-20k,1847,1774,0.1153,0.1201,6.82,0.244,3558.0,0.3241
102 +10-Q,unregistered_sales,f20-40k,569,538,0.2074,0.2193,11.42,0.187,4810.0,0.4311
103 +10-Q,unregistered_sales,g40-80k,260,260,0.6654,0.6654,27.31,0.113,6399.0,1.0398
104 +10-Q,unregistered_sales,h>=80k,571,569,0.2487,0.2496,16.11,0.055,6777.0,0.3669
105 +8-K,full_body,a<3k,91457,23244,0.0649,0.2552,6.21,1.0,2546.0,0.2547
106 +8-K,full_body,b3-6k,225916,106791,0.1281,0.2709,11.4,1.0,4063.0,0.3152
107 +8-K,full_body,c6-9k,47878,40262,0.4634,0.5511,37.21,1.0,7229.0,0.641
108 +8-K,full_body,d9-12k,17948,17023,0.3668,0.3868,27.7,0.348,3565.0,1.0289
109 +8-K,full_body,e12-20k,12599,12331,0.5425,0.5543,38.92,0.313,4581.0,1.1842
110 +8-K,full_body,f20-40k,3859,3847,0.8785,0.8812,54.6,0.24,6209.0,1.4148
111 +8-K,full_body,g40-80k,759,759,0.7457,0.7457,44.53,0.136,6933.0,1.0756
112 +8-K,full_body,h>=80k,185,185,0.8919,0.8919,45.95,0.048,6668.0,1.3375
added results/focus/shown_fraction_bins.csv +10 −0
@@ -0,0 +1,10 @@
1 +form,bin,n,calls,with_mentions,mentions
2 +10-K,a>=0.9,58527,50151,3900,7509
3 +10-K,b0.25-0.9,10276,10258,2136,5541
4 +10-K,c<0.25,74901,74901,24841,68572
5 +10-Q,a>=0.9,373347,158886,15093,19490
6 +10-Q,b0.25-0.9,23708,23354,5810,12211
7 +10-Q,c<0.25,181738,181733,54802,120560
8 +8-K,a>=0.9,365776,170302,49247,57058
9 +8-K,b0.25-0.9,21524,20839,9537,13252
10 +8-K,c<0.25,13301,13301,2864,4284
added results/focus/stats_keys.json +18 −0
@@ -0,0 +1,18 @@
1 +{
2 + "ledger_10k_mdna_median_words": 9905.0,
3 + "ledger_10k_mdna_median_chars": 64194.0,
4 + "ledger_10k_mdna_n": 28919,
5 + "ledger_10k_mdna_shown_p50": 0.09323377723039027,
6 + "ledger_10k_mdna_last_p50": 0.16183513990420972,
7 + "ledger_10k_mdna_pct_shown_lt_25": 90.50451260417027,
8 + "ledger_sections_over_9000_chars": 326095,
9 + "ledger_sections": 1123098,
10 + "calls_over_9000_chars": 324386,
11 + "calls": 703725,
12 + "mentions_from_sections_shown_lt_25": 193416,
13 + "mentions_total": 308477,
14 + "msg_truncated_14k": 0,
15 + "gate_py_pass_total": 703737,
16 + "gate_duckdb_pass_total_N1": 703737,
17 + "mean_shown_frac_over_budget": 0.15934812827563005
18 +}
\ No newline at end of file
19
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