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N3: resolver composition, over-merge indicators, specification curve, ties, June->October reconciliation

Items: FIRC0001-ADVE-01 EDIT-07 THEO-01 AI_C-02 ECON-02 DATA-11 ECON-16 THEO-17 EDIT-06.
_keyed rebuilt from resolve.py:17-32 (121,099 projects, identical id set); 9 keys x 5 thresholds;
project count 21,027 (cik|type) .. 96,749 (type dropped) .. 121,099 (current) .. 198,488 (strict) .. 308,065 (mention);
token 'acquisition' absorbs 40,407 mentions; 33,820 projects tie at max confidence; June block reproduces 39,930 / 19,227.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent 1d2705d Browse files

15 changed files +1,753 −0

added code/audit/a03_resolver_speccurve.py +257 −0
@@ -0,0 +1,257 @@
1 +"""N3 - resolver composition, over-merge indicators, specification curve, ties, June->October
2 +reconciliation (cluster B). Read-only; _m/_keyed rebuilt as TEMP tables from resolve.py:17-32.
3 +"""
4 +from __future__ import annotations
5 +import json, sys
6 +from pathlib import Path
7 +sys.path.insert(0, str(Path(__file__).resolve().parent))
8 +from common import *
9 +sys.path.insert(0, str(Path(__file__).resolve().parent / "spid"))
10 +import taxonomy as tx # snapshot copy (hash-verified in N0)
11 +
12 +EXAMPLE_PROJECT = "3e2705ba5b2dd27408b25f0145e34250" # stats.json example_project (Cheniere Corpus Christi Stage 3)
13 +STOP = ["project", "program", "development", "expansion", "acquisition", "phase", "initiative", "facility", "construction"]
14 +THRESHOLDS = [0.45, 0.6, 0.7, 0.8, 0.9]
15 +
16 +r = Run("resolver")
17 +con = connect(SPID_DB)
18 +tmin = con.execute("select min(extracted_at) from extracted_sections").fetchone()[0]
19 +con.execute(f"""CREATE TEMP TABLE runmap AS SELECT section_id, CASE WHEN extracted_at = TIMESTAMP '{tmin}' THEN 'backfilled_june' ELSE 'october' END AS run FROM extracted_sections""")
20 +
21 +# ---- exact rebuild of _m / _keyed (resolve.py:17-32) --------------------------------------------
22 +con.execute("""
23 +CREATE TEMP TABLE _m AS
24 +SELECT *,
25 + lower(coalesce(try(locations[1]), '')) AS loc1,
26 + regexp_extract(lower(regexp_replace(project_name, '[^A-Za-z ]', '', 'g')), '([a-z]{4,})', 1) AS name_tok
27 +FROM project_mentions""")
28 +con.execute("""
29 +CREATE TEMP TABLE _keyed AS
30 +SELECT *,
31 + cik || '|' || project_type || '|' || CASE WHEN loc1 <> '' THEN loc1 ELSE coalesce(nullif(name_tok,''),'general') END AS gkey,
32 + md5(cik || '|' || project_type || '|' || CASE WHEN loc1 <> '' THEN loc1 ELSE coalesce(nullif(name_tok,''),'general') END) AS project_id
33 +FROM _m""")
34 +V = {}
35 +V["rebuilt_projects"] = con.execute("select count(distinct project_id) from _keyed").fetchone()[0]
36 +V["stored_projects"] = con.execute("select count(*) from projects").fetchone()[0]
37 +V["project_id_set_equal"] = con.execute("select count(*) from (select project_id from _keyed except select project_id from projects) union all select count(*) from (select project_id from projects except select distinct project_id from _keyed)").fetchall()
38 +V["mentions"] = con.execute("select count(*) from _keyed").fetchone()[0]
39 +
40 +# ---- (a) key composition ----------------------------------------------------------------------
41 +V["mentions_by_key_branch"] = con.execute("""
42 + select case when loc1<>'' then 'location' when nullif(name_tok,'') is not null then 'name_token' else 'general' end b, count(*) from _keyed group by 1""").fetchall()
43 +V["projects_by_key_branch"] = con.execute("""
44 + select case when loc1<>'' then 'location' when nullif(name_tok,'') is not null then 'name_token' else 'general' end b, count(distinct project_id) from _keyed group by 1""").fetchall()
45 +V["projects_keyed_general"] = con.execute("select count(distinct project_id) from _keyed where gkey like '%|general'").fetchone()[0]
46 +V["mentions_keyed_general"] = con.execute("select count(*) from _keyed where gkey like '%|general'").fetchone()[0]
47 +states = sorted(tx.US_STATES); countries = sorted(tx.COUNTRIES)
48 +con.execute("CREATE TEMP TABLE geo AS SELECT unnest(?) AS loc, 'us_state' AS kind UNION ALL SELECT unnest(?), 'country'", [states, countries])
49 +V["location_keyed_projects_by_geo_kind"] = con.execute("""
50 + with p as (select project_id, any_value(loc1) loc1 from _keyed where loc1<>'' group by 1)
51 + select coalesce(g.kind,'other_place') kind, count(*) from p left join geo g on p.loc1 = g.loc group by 1""").fetchall()
52 +V["location_keyed_mentions_by_geo_kind"] = con.execute("""
53 + select coalesce(g.kind,'other_place') kind, count(*) from _keyed k left join geo g on k.loc1 = g.loc where k.loc1<>'' group by 1""").fetchall()
54 +r.dump("top_name_tokens.csv", con, """
55 + with t as (select name_tok, count(*) mentions, count(distinct project_id) projects from _keyed where loc1='' and nullif(name_tok,'') is not null group by 1)
56 + select name_tok, mentions, projects, round(100.0*mentions/(select count(*) from _keyed where loc1=''),2) pct_of_tokenkeyed_mentions,
57 + round(100.0*projects/(select count(distinct project_id) from _keyed where loc1=''),2) pct_of_tokenkeyed_projects
58 + from t order by projects desc limit 50""")
59 +r.dump("top_location_keys.csv", con, """
60 + select loc1, count(*) mentions, count(distinct project_id) projects from _keyed where loc1<>'' group by 1 order by 2 desc limit 50""")
61 +r.dump("key_branch_by_type.csv", con, """
62 + select project_type, count(distinct project_id) projects,
63 + count(distinct project_id) filter (where loc1<>'') loc_keyed,
64 + count(distinct project_id) filter (where loc1='' and nullif(name_tok,'') is not null) token_keyed,
65 + count(distinct project_id) filter (where loc1='' and nullif(name_tok,'') is null) general_keyed
66 + from _keyed group by 1 order by 2 desc""")
67 +
68 +# ---- (b) over-merge indicators ----------------------------------------------------------------
69 +con.execute("""
70 +CREATE TEMP TABLE pstat AS
71 +SELECT project_id, any_value(project_type) project_type, count(*) n_mentions,
72 + count(DISTINCT lower(trim(project_name))) n_names,
73 + count(DISTINCT lower(trim(coalesce(try(partners[1]), '')))) FILTER (WHERE len(partners)>0) n_partners,
74 + count(DISTINCT accession_number) n_filings, count(DISTINCT cik) n_ciks,
75 + count(DISTINCT lower(description)) n_desc
76 +FROM _keyed GROUP BY 1""")
77 +bucket = "case when n_mentions=1 then '1' when n_mentions=2 then '2' when n_mentions<=5 then '3-5' when n_mentions<=10 then '6-10' else '>10' end"
78 +r.dump("overmerge_by_type.csv", con, f"""
79 + select project_type, count(*) projects, sum(n_mentions) mentions,
80 + round(100.0*count(*) filter (where n_names>=3)/count(*),2) pct_ge3_names,
81 + round(100.0*count(*) filter (where n_names>=2)/count(*),2) pct_ge2_names,
82 + round(100.0*count(*) filter (where n_partners>=3)/nullif(count(*) filter (where n_partners>=1),0),2) pct_ge3_partners_among_with_partner,
83 + count(*) filter (where n_partners>=3) n_ge3_partners,
84 + round(100.0*count(*) filter (where n_mentions>1 and n_names = n_mentions)/nullif(count(*) filter (where n_mentions>1),0),2) pct_multi_all_names_distinct
85 + from pstat group by 1 order by 2 desc""")
86 +r.dump("overmerge_by_bucket.csv", con, f"""
87 + select {bucket} bucket, count(*) projects,
88 + round(100.0*count(*) filter (where n_names>=3)/count(*),2) pct_ge3_names,
89 + round(100.0*count(*) filter (where n_partners>=3)/nullif(count(*) filter (where n_partners>=1),0),2) pct_ge3_partners_among_with_partner,
90 + round(avg(n_names),2) mean_names
91 + from pstat group by 1 order by min(n_mentions)""")
92 +r.dump("overmerge_ma_by_bucket.csv", con, f"""
93 + select {bucket} bucket, count(*) projects,
94 + count(*) filter (where n_partners>=1) with_partner,
95 + round(100.0*count(*) filter (where n_partners>=3)/nullif(count(*) filter (where n_partners>=1),0),2) pct_ge3_partners,
96 + round(100.0*count(*) filter (where n_partners>=2)/nullif(count(*) filter (where n_partners>=1),0),2) pct_ge2_partners,
97 + round(100.0*count(*) filter (where n_names>=3)/count(*),2) pct_ge3_names
98 + from pstat where project_type='ma_integration' group by 1 order by min(n_mentions)""")
99 +# amount from a mention naming a different counterparty than the representative mention
100 +V["amount_from_other_counterparty"] = con.execute("""
101 + with rep as (select project_id, arg_max(lower(trim(coalesce(try(partners[1]),''))), confidence) rep_partner from _keyed group by 1),
102 + amt as (select project_id, arg_max(lower(trim(coalesce(try(partners[1]),''))), amount_usd) amt_partner, max(amount_usd) mx from _keyed where amount_usd is not null group by 1)
103 + select count(*) filter (where rep_partner<>'' and amt_partner<>'' and rep_partner<>amt_partner) differing,
104 + count(*) filter (where rep_partner<>'' and amt_partner<>'') both_named, count(*) with_amount
105 + from amt join rep using(project_id)""").fetchone()
106 +V["amount_from_other_counterparty_ma"] = con.execute("""
107 + with rep as (select project_id, any_value(project_type) t, arg_max(lower(trim(coalesce(try(partners[1]),''))), confidence) rep_partner from _keyed group by 1),
108 + amt as (select project_id, arg_max(lower(trim(coalesce(try(partners[1]),''))), amount_usd) amt_partner from _keyed where amount_usd is not null group by 1)
109 + select count(*) filter (where rep_partner<>'' and amt_partner<>'' and rep_partner<>amt_partner) differing,
110 + count(*) filter (where rep_partner<>'' and amt_partner<>'') both_named, count(*) with_amount
111 + from amt join rep using(project_id) where t='ma_integration'""").fetchone()
112 +V["amount_from_other_name"] = con.execute("""
113 + with rep as (select project_id, arg_max(lower(trim(project_name)), confidence) rep_name from _keyed group by 1),
114 + amt as (select project_id, arg_max(lower(trim(project_name)), amount_usd) amt_name from _keyed where amount_usd is not null group by 1)
115 + select count(*) filter (where rep_name<>amt_name) differing, count(*) with_amount from amt join rep using(project_id)""").fetchone()
116 +r.dump("example_provenance_keys.csv", con, """
117 + select project_id, cik, project_type, gkey, count(*) n, count(distinct lower(project_name)) n_names, list(distinct left(project_name,60)) names
118 + from _keyed where project_id in (select project_id from projects where project_id in (
119 + select project_id from project_timeline group by 1 having count(*) between 4 and 8) and total_amount_usd > 5e8 and project_type in ('plant_construction','data_center'))
120 + group by 1,2,3,4 order by n desc limit 30""")
121 +
122 +# ---- (c) specification curve ----------------------------------------------------------------------
123 +stop_sql = ",".join("'" + s + "'" for s in STOP)
124 +con.execute(f"""
125 +CREATE TEMP TABLE km AS
126 +SELECT mention_id, cik, project_type, confidence, loc1, name_tok, gkey,
127 + regexp_replace(trim(regexp_replace(lower(project_name), '[^a-z0-9 ]', '', 'g')), ' +', ' ', 'g') AS name_norm,
128 + list_filter(string_split(regexp_replace(lower(project_name), '[^a-z ]', '', 'g'), ' '), lambda x: length(x) >= 4) AS toks,
129 + list_filter(string_split(regexp_replace(lower(project_name), '[^a-z ]', '', 'g'), ' '), lambda x: length(x) >= 4 AND x NOT IN ({stop_sql})) AS toks_sw,
130 + trim(regexp_replace(regexp_replace(regexp_replace(loc1, '\\b(u\\.s\\.|usa|u\\.s\\.a\\.|united states of america)\\b', 'united states', 'g'), '[^a-z, ]', '', 'g'), ' +', ' ', 'g')) AS loc_norm
131 +FROM _keyed""")
132 +con.execute("""
133 +CREATE TEMP TABLE keys AS
134 +SELECT mention_id, confidence, project_type,
135 + cik || '|' || project_type AS k_cik_type,
136 + gkey AS k_current,
137 + cik || '|' || project_type || '|' || CASE WHEN loc1 <> '' THEN loc1 ELSE coalesce(toks_sw[1], 'general') END AS k_current_stopwords,
138 + cik || '|' || project_type || '|' || CASE WHEN loc_norm <> '' THEN trim(string_split(loc_norm, ',')[1]) ELSE coalesce(toks_sw[1], 'general') END AS k_city_normalised,
139 + cik || '|' || CASE WHEN loc1 <> '' THEN loc1 ELSE coalesce(nullif(name_tok,''), 'general') END AS k_type_dropped,
140 + cik || '|' || project_type || '|' || CASE WHEN loc1 <> '' THEN loc1 ELSE coalesce(toks_sw[1], 'general') || '|' || coalesce(toks_sw[2], '') END AS k_two_tokens,
141 + cik || '|' || project_type || '|' || coalesce(toks_sw[1], 'general') AS k_name_only,
142 + cik || '|' || project_type || '|' || name_norm || '|' || loc1 AS k_strict,
143 + mention_id AS k_mention_level
144 +FROM km""")
145 +KEYS = ["k_cik_type", "k_type_dropped", "k_current", "k_current_stopwords", "k_city_normalised", "k_name_only", "k_two_tokens", "k_strict", "k_mention_level"]
146 +ex_mention = con.execute("select arg_max(mention_id, confidence) from _keyed where project_id = ?", [EXAMPLE_PROJECT]).fetchone()[0]
147 +rows = []; bytype = []
148 +for k in KEYS:
149 + for t in THRESHOLDS:
150 + q = f"""
151 + with g as (select {k} key, count(*) n, max(mention_id = '{ex_mention}')::int has_ex from keys where confidence >= {t} group by 1)
152 + select count(*) projects, sum(n) mentions, round(100.0*count(*) filter (where n=1)/count(*),2) single_pct,
153 + round(100.0*count(*) filter (where n>10)/count(*),2) gt10_pct, round(avg(n),3) mean_mentions,
154 + max(n) max_cluster, coalesce(max(n) filter (where has_ex=1),0) example_cluster_size
155 + from g"""
156 + row = con.execute(q).fetchone()
157 + rows.append((k, t) + tuple(row))
158 + bt = con.execute(f"""
159 + with g as (select {k} key, any_value(project_type) project_type, count(*) n from keys where confidence >= {t} group by 1)
160 + select project_type, count(*) n from g group by 1""").fetchall()
161 + bytype += [(k, t, pt, n) for pt, n in bt]
162 + print(k, t, row, flush=True)
163 +r.write_csv("spec_curve.csv", ["key", "threshold", "projects", "mentions", "single_mention_pct", "gt10_mention_pct", "mean_mentions", "max_cluster", "example_cluster_size_cheniere"], rows)
164 +r.write_csv("spec_curve_by_type.csv", ["key", "threshold", "project_type", "projects"], bytype)
165 +cur = [x for x in rows if x[0] == "k_current" and x[1] == 0.45][0]
166 +V["project_count_current"] = cur[2]
167 +V["project_count_range_045"] = {k: [x[2] for x in rows if x[0] == k and x[1] == 0.45][0] for k in KEYS}
168 +V["project_count_strict_045"] = V["project_count_range_045"]["k_strict"]
169 +V["project_count_loose_045_cik_type"] = V["project_count_range_045"]["k_cik_type"]
170 +V["example_mention"] = ex_mention
171 +
172 +# ---- (d) ties and idempotence ----------------------------------------------------------------------
173 +D = {}
174 +D["projects_with_tie_at_max_confidence"] = con.execute("""
175 + with mx as (select project_id, max(confidence) mc from _keyed group by 1)
176 + select count(*) from (select k.project_id from _keyed k join mx using(project_id) where k.confidence = mx.mc group by 1 having count(*)>1)""").fetchone()[0]
177 +D["projects_with_tie_and_distinct_names"] = con.execute("""
178 + with mx as (select project_id, max(confidence) mc from _keyed group by 1)
179 + select count(*) from (select k.project_id from _keyed k join mx using(project_id) where k.confidence = mx.mc group by 1 having count(distinct project_name)>1)""").fetchone()[0]
180 +D["projects_with_tie_and_distinct_descriptions"] = con.execute("""
181 + with mx as (select project_id, max(confidence) mc from _keyed group by 1)
182 + select count(*) from (select k.project_id from _keyed k join mx using(project_id) where k.confidence = mx.mc group by 1 having count(distinct description)>1)""").fetchone()[0]
183 +D["projects_with_status_tie_on_last_seen"] = con.execute("""
184 + with mx as (select project_id, max(filing_date) md from _keyed group by 1)
185 + select count(*) from (select k.project_id from _keyed k join mx using(project_id) where k.filing_date = mx.md group by 1 having count(distinct status)>1)""").fetchone()[0]
186 +D["mentions_at_confidence_1"] = con.execute("select count(*) from project_mentions where confidence = 1.0").fetchone()[0]
187 +# stored projects vs rebuilt with the original (tie-ambiguous) SQL: how many representative fields differ
188 +con.execute("""
189 +CREATE TEMP TABLE rebuilt AS
190 +SELECT project_id, arg_max(project_name, confidence) project_name, arg_max(status, filing_date) status, arg_max(description, confidence) description,
191 + max(amount_usd) total_amount_usd, count(*) n_mentions
192 +FROM _keyed GROUP BY 1""")
193 +D["rebuilt_vs_stored_diff"] = con.execute("""
194 + select count(*) filter (where r.project_name is distinct from p.project_name) name_diff,
195 + count(*) filter (where r.status is distinct from p.status) status_diff,
196 + count(*) filter (where r.description is distinct from p.description) desc_diff,
197 + count(*) filter (where r.total_amount_usd is distinct from p.total_amount_usd) amount_diff,
198 + count(*) filter (where r.n_mentions is distinct from p.n_mentions) n_diff, count(*) n
199 + from rebuilt r join projects p using(project_id)""").fetchone()
200 +con.execute("""
201 +CREATE TEMP TABLE rebuilt2 AS
202 +SELECT project_id, arg_max(project_name, confidence) project_name, arg_max(status, filing_date) status, arg_max(description, confidence) description
203 +FROM _keyed GROUP BY 1""")
204 +D["original_sql_run_twice_diff"] = con.execute("""
205 + select count(*) filter (where a.project_name is distinct from b.project_name), count(*) filter (where a.status is distinct from b.status), count(*) filter (where a.description is distinct from b.description)
206 + from rebuilt a join rebuilt2 b using(project_id)""").fetchone()
207 +det = """
208 +SELECT project_id, project_name, status, description FROM (
209 + SELECT project_id,
210 + first_value(project_name) OVER (PARTITION BY project_id ORDER BY confidence DESC, mention_id) project_name,
211 + first_value(description) OVER (PARTITION BY project_id ORDER BY confidence DESC, mention_id) description,
212 + first_value(status) OVER (PARTITION BY project_id ORDER BY filing_date DESC, mention_id) status,
213 + row_number() OVER (PARTITION BY project_id ORDER BY mention_id) rn
214 + FROM _keyed) WHERE rn = 1"""
215 +con.execute(f"CREATE TEMP TABLE det1 AS {det}")
216 +con.execute(f"CREATE TEMP TABLE det2 AS {det}")
217 +D["deterministic_tiebreak_run_twice_diff"] = con.execute("""
218 + select count(*) filter (where a.project_name is distinct from b.project_name), count(*) filter (where a.status is distinct from b.status), count(*) filter (where a.description is distinct from b.description)
219 + from det1 a join det2 b using(project_id)""").fetchone()
220 +D["deterministic_vs_stored_diff"] = con.execute("""
221 + select count(*) filter (where a.project_name is distinct from p.project_name), count(*) filter (where a.status is distinct from p.status), count(*) filter (where a.description is distinct from p.description)
222 + from det1 a join projects p using(project_id)""").fetchone()
223 +r.write_json("ties_idempotence.json", D)
224 +
225 +# ---- (e) June -> October reconciliation (S&P 500 CIKs) -------------------------------------------
226 +con.execute("""CREATE TEMP TABLE kr AS SELECT k.*, rm.run, co.in_sp500, co.in_russell3000 FROM _keyed k LEFT JOIN runmap rm USING (section_id) LEFT JOIN src_companies co ON k.cik = co.cik""")
227 +R = {}
228 +R["sp500_ciks"] = con.execute("select count(*) from src_companies where in_sp500").fetchone()[0]
229 +R["mentions_in_backfilled_block"] = con.execute("select count(*) from kr where run='backfilled_june'").fetchone()[0]
230 +R["projects_from_backfilled_block_only"] = con.execute("select count(distinct project_id) from kr where run='backfilled_june'").fetchone()[0]
231 +R["expected_june"] = {"mentions": 39930, "projects": 19227, "timeline_rows": 39930}
232 +R["sp500_mentions_total"] = con.execute("select count(*) from kr where in_sp500").fetchone()[0]
233 +R["sp500_projects_total"] = con.execute("select count(distinct project_id) from kr where in_sp500").fetchone()[0]
234 +R["paper_sp500_projects_october"] = 27766
235 +R["sp500_projects_by_run_composition"] = con.execute("""
236 + with p as (select project_id, max(run='backfilled_june')::int has_june, max(run='october')::int has_oct from kr where in_sp500 group by 1)
237 + select case when has_june=1 and has_oct=1 then 'both runs' when has_june=1 then 'june only' else 'october only' end c, count(*) from p group by 1""").fetchall()
238 +R["sp500_mentions_by_run_form"] = con.execute("select run, form_type, count(*), count(distinct accession_number) from kr where in_sp500 group by 1,2 order by 1,2").fetchall()
239 +R["sp500_mentions_filing_date_le_20260525_by_run"] = con.execute("select run, count(*), count(distinct project_id) from kr where in_sp500 and filing_date <= DATE '2026-05-25' group by 1").fetchall()
240 +R["sp500_projects_filing_date_le_20260525"] = con.execute("select count(distinct project_id) from kr where in_sp500 and filing_date <= DATE '2026-05-25'").fetchone()[0]
241 +R["backfilled_block_mentions_not_sp500"] = con.execute("select count(*) from kr where run='backfilled_june' and not coalesce(in_sp500,false)").fetchone()[0]
242 +R["june_10q_accessions_in_block_ledger"] = con.execute(f"""
243 + select count(distinct e.accession_number) from extracted_sections e join src_filings f using(accession_number) where e.extracted_at = TIMESTAMP '{tmin}' and f.form_type='10-Q'""").fetchone()[0]
244 +R["ledger_block_accessions_by_form"] = con.execute(f"""
245 + select f.form_type, count(distinct e.accession_number), count(*) from extracted_sections e join src_filings f using(accession_number) where e.extracted_at = TIMESTAMP '{tmin}' group by 1 order by 1""").fetchall()
246 +R["sp500_october_ledger_by_form"] = con.execute(f"""
247 + select f.form_type, count(distinct e.accession_number), count(*), sum(e.n_mentions) from extracted_sections e join src_filings f using(accession_number) join src_companies co on f.cik=co.cik
248 + where e.extracted_at <> TIMESTAMP '{tmin}' and co.in_sp500 group by 1 order by 1""").fetchall()
249 +R["sp500_block_ledger_by_form"] = con.execute(f"""
250 + select f.form_type, count(distinct e.accession_number), count(*), sum(e.n_mentions) from extracted_sections e join src_filings f using(accession_number) join src_companies co on f.cik=co.cik
251 + where e.extracted_at = TIMESTAMP '{tmin}' and co.in_sp500 group by 1 order by 1""").fetchall()
252 +r.write_json("june_october_reconciliation.json", R)
253 +r.dump("june_october_reconciliation.csv", con, """
254 + select coalesce(in_sp500,false) in_sp500, run, form_type, count(*) mentions, count(distinct project_id) projects_touched, count(distinct accession_number) filings, count(distinct cik) issuers
255 + from kr group by 1,2,3 order by 1,2,3""")
256 +r.write_json("composition.json", V)
257 +r.finish()
added results/resolver/composition.json +100 −0
@@ -0,0 +1,100 @@
1 +{
2 + "rebuilt_projects": 121099,
3 + "stored_projects": 121099,
4 + "project_id_set_equal": [
5 + [
6 + 0
7 + ],
8 + [
9 + 0
10 + ]
11 + ],
12 + "mentions": 308065,
13 + "mentions_by_key_branch": [
14 + [
15 + "name_token",
16 + 187212
17 + ],
18 + [
19 + "location",
20 + 118783
21 + ],
22 + [
23 + "general",
24 + 2070
25 + ]
26 + ],
27 + "projects_by_key_branch": [
28 + [
29 + "name_token",
30 + 66405
31 + ],
32 + [
33 + "location",
34 + 54658
35 + ],
36 + [
37 + "general",
38 + 229
39 + ]
40 + ],
41 + "projects_keyed_general": 286,
42 + "mentions_keyed_general": 2152,
43 + "location_keyed_projects_by_geo_kind": [
44 + [
45 + "other_place",
46 + 40422
47 + ],
48 + [
49 + "us_state",
50 + 6110
51 + ],
52 + [
53 + "country",
54 + 8126
55 + ]
56 + ],
57 + "location_keyed_mentions_by_geo_kind": [
58 + [
59 + "other_place",
60 + 78479
61 + ],
62 + [
63 + "country",
64 + 22986
65 + ],
66 + [
67 + "us_state",
68 + 17318
69 + ]
70 + ],
71 + "amount_from_other_counterparty": [
72 + 2347,
73 + 21908,
74 + 56500
75 + ],
76 + "amount_from_other_counterparty_ma": [
77 + 1630,
78 + 12372,
79 + 28044
80 + ],
81 + "amount_from_other_name": [
82 + 8882,
83 + 56500
84 + ],
85 + "project_count_current": 121099,
86 + "project_count_range_045": {
87 + "k_cik_type": 21027,
88 + "k_type_dropped": 96749,
89 + "k_current": 121099,
90 + "k_current_stopwords": 130101,
91 + "k_city_normalised": 123246,
92 + "k_name_only": 114244,
93 + "k_two_tokens": 145697,
94 + "k_strict": 198488,
95 + "k_mention_level": 308065
96 + },
97 + "project_count_strict_045": 198488,
98 + "project_count_loose_045_cik_type": 21027,
99 + "example_mention": "cebf8e727e1a7f5bc821"
100 +}
\ No newline at end of file
added results/resolver/example_provenance_keys.csv +31 −0
@@ -0,0 +1,31 @@
1 +project_id,cik,project_type,gkey,n,n_names,names
2 +dbe60cbe1feab59090d7ce4c63c0b6a1,0001001838,plant_construction,0001001838|plant_construction|peru,8,4,"['Tía María Mining Project', 'Tia Maria Greenfield Project', 'Tia Maria Project', 'Toquepala Expansion Project']"
3 +7942bcad2b16e5c326d64ef7cc4d79f6,0000092122,plant_construction,0000092122|plant_construction|waynesboro/georgia/usa,8,1,['Plant Vogtle Units 3 and 4']
4 +d333f4d6faa95492939e7d242761e7b5,0000753308,plant_construction,0000753308|plant_construction|california,8,7,"['507 Megawatt Natural Gas-Fired Power Generation Facility', 'McCoy Project', '20 MW Solar Facility', 'Redwood Trails Wind Generation Facilities', 'Desert Sunlight Solar Project', 'Genesis Project', 'Genesis Solar Project']"
5 +f58417bd7fc1be9b73fcd47a88f4a406,0001513761,plant_construction,0001513761|plant_construction|norwegian,8,6,"['Norwegian Breakaway', 'Norwegian Escape and Norwegian Bliss', 'Norwegian Joy and Breakaway Plus Class Ships', 'Norwegian Getaway', 'Norwegian Bliss', 'Norwegian Encore']"
6 +105ebf2e17ec61bd2bff6f3cce50a33b,0001451505,plant_construction,0001451505|plant_construction|deepwater,8,7,"['Deepwater Titan', 'Deepwater Champion', 'Deepwater Poseidon', 'Deepwater Thalassa Construction', 'Deepwater Atlas', 'Deepwater Pontus', 'Deepwater Proteus']"
7 +47fda6961d383537e6e3ce70adf8b064,0001324404,plant_construction,"0001324404|plant_construction|donaldsonville, louisiana",8,6,"['Green Hydrogen Project', 'Ammonia and Urea Production Units', 'New Ammonia and Urea Plants', 'Ammonia and UAN Plants Construction', 'Capacity Expansion Projects', 'Urea Plant Commissioning']"
8 +dc8940627597a296318115e21e7ea024,0001164727,plant_construction,0001164727|plant_construction|peru,8,2,"['Conga Project', 'Conga']"
9 +00461ab6a8ee44d809b436b3aaf59072,0001287865,plant_construction,0001287865|plant_construction|development,8,4,"['Development Project', 'Development Projects with First Choice Emergency Room', 'Development Projects with Adeptus Health, Inc.', 'Development Projects']"
10 +5317e0ee56e32e3563b0d34a8ec04442,0000025232,plant_construction,0000025232|plant_construction|development,8,5,"['Development Pipeline', 'New Development Projects', 'Development of Domain 10, Domain 12, and Avalon 10000', 'Development projects', 'Development of Office and Mixed-Use Projects', 'Development Projects']"
11 +3e2705ba5b2dd27408b25f0145e34250,0000003570,plant_construction,"0000003570|plant_construction|san patricio county, texas",8,7,"['Third Liquefaction Train', 'First Liquefaction Train Construction', 'Third Liquefaction Train at CCLNG', 'Corpus Christi Liquefaction Stage 3 Project', 'Second Liquefaction Train at CCLNG', 'Corpus Christi Liquefaction Project', 'Second Liquefaction Train']"
12 +30a198e7029a4956be8327cd83da4b70,0001513761,plant_construction,0001513761|plant_construction|explorer,7,3,"['Explorer Class Newbuild Loan Agreement', 'Explorer Class Ships', 'Explorer Class Ship']"
13 +14d587c2883840856f231fe3414727b5,0000831259,plant_construction,0000831259|plant_construction|grasberg,7,4,"['Grasberg Underground Development', 'Grasberg Block Cave Restart', 'Underground Development Activities at Grasberg', 'Grasberg Underground Mine Development']"
14 +fbfb8fca9ac08121c1681b13a9db3e38,0001051470,plant_construction,0001051470|plant_construction|construction,7,2,"['Construction of New Communications Infrastructure', 'Construction of Small Cells']"
15 +7b5cdbd8d0e9a2a0519d7093bd3c78a9,0000884887,plant_construction,0000884887|plant_construction|project,7,4,"['Project Edge', 'Project Sunshine Ship 1', 'Project Sunshine Ship 2', 'Project Sunshine']"
16 +28b552d43d7800a5e536b8c66bfd36fd,0000022444,plant_construction,"0000022444|plant_construction|berkeley county, west virginia",7,3,"['Fourth Micro Mill', 'Fourth Micro Mill Construction', 'Berkeley County Micro Mill']"
17 +eee28ac6c870400cb50b2119bd8e6c8e,0001040829,plant_construction,0001040829|plant_construction|aurora/colorado/usa,7,4,"['Aurora Resort and Convention Hotel', 'Gaylord Rockies expansion', 'Aurora Resort and Convention Hotel Development', 'Gaylord Rockies Resort & Convention Center']"
18 +f2126501fc9d07f93ce25627ba093984,0000034903,plant_construction,"0000034903|plant_construction|somerville, massachusetts",7,4,"['Assembly Row', 'Assembly Row Phase II', 'Assembly Square', 'Assembly Row Phase I']"
19 +a9a63ffa287236133e060b110818add5,0000833079,plant_construction,0000833079|plant_construction|land,7,1,['Land Development and Home Construction']
20 +7ba90cf2bc3e1cbbe5e26a2d8d8d7835,0001167419,data_center,0001167419|data_center|rockdale,7,3,"['Rockdale Facility', '191 Critical IT MW Data Center Project', 'Rockdale Facility Expansion']"
21 +83b669268f21ba09eae690226d209c01,0000915912,plant_construction,0000915912|plant_construction|future,7,2,"['Future Apartment Community Developments', 'Future Apartment Communities Development']"
22 +7473bc6fc5d414ab5ee5b287418491f1,0001795250,plant_construction,0001795250|plant_construction|las vegas/nevada/usa,7,2,"['Sphere in Las Vegas', 'MSG Sphere']"
23 +d2c21e2aa718297425664d398b9a4d0b,0000745732,plant_construction,0000745732|plant_construction|distribution,7,5,"['Distribution Centers Construction', 'New Distribution Centers', 'Distribution Center Expansion', 'Distribution Center Construction', 'New Distribution Center Construction']"
24 +6d7dca59c2d0c185240335f3a8060f25,0001653653,plant_construction,0001653653|plant_construction|las vegas/nevada/usa,7,5,"['Palace Station Upgrade and Expansion', 'Durango Casino Resort', 'New Casino Resort Construction', 'Durango Casino & Resort', 'Durango']"
25 +2a7deb96cd6e799cf0a00b78cddd55e6,0001653653,plant_construction,0001653653|plant_construction|las vegas/nevada,7,6,"['Palms Casino Resort Redevelopment', 'Durango Casino Resort', 'Palms Redevelopment Project', 'Palace Station Redevelopment', 'Durango Development', 'Palace Station Redevelopment Project']"
26 +13fec26e29e1bb20856eb321aadc6344,0000104169,plant_construction,0000104169|plant_construction|store,6,2,"['Store Remodels', 'Store and Club Remodels']"
27 +0a57f5fcba583f08d49dfcdb13b7e654,0000726728,plant_construction,0000726728|plant_construction|properties,6,2,"['Properties under development', 'Properties Under Development or Expansion']"
28 +4ec04c01a2e25d3816161f7f36aa936c,0001575965,plant_construction,0001575965|plant_construction|chicago,6,3,"['Bally’s Chicago', 'Bally’s Chicago Casino Resort', ""Bally's Chicago Development""]"
29 +6070161ae6a31d8613aa7e782212a253,0001612720,plant_construction,0001612720|plant_construction|grande,6,5,"['Rio Grande Natural Gas Liquefaction Facility', 'Rio Grande LNG Liquefaction and Export Project', 'Rio Grande Natural Gas Liquefaction Facility - Train 3', 'Rio Grande LNG Train 5', 'Rio Grande LNG Train 4']"
30 +61699e8c51c4cd75ceeef401fad61228,0001039684,plant_construction,"0001039684|plant_construction|texas city, texas",6,5,"['Texas City Liquified Petroleum Gas Export Terminal', 'Liquefied Petroleum Gas Export Terminal', 'Liquified Petroleum Gas Export Terminal and Pipeline', 'Texas City Logistics export terminal', 'Joint Ventures for LPG Export Terminal']"
31 +fe50af95e5803a7d904ff3430c127017,0000003570,plant_construction,0000003570|plant_construction|cameron parish,6,5,"['SPL Project', 'Liquefaction Facilities Construction', 'Fourth Liquefaction Train', 'Second LNG Liquefaction Train', 'Fifth Liquefaction Train']"
added results/resolver/june_october_reconciliation.csv +10 −0
@@ -0,0 +1,10 @@
1 +in_sp500,run,form_type,mentions,projects_touched,filings,issuers
2 +False,october,10-K,64798,34658,15784,1857
3 +False,october,10-Q,117224,45274,40745,1954
4 +False,october,8-K,55676,34420,45937,1964
5 +True,backfilled_june,10-K,15787,8697,4193,481
6 +True,backfilled_june,10-Q,5817,2071,2028,84
7 +True,backfilled_june,8-K,18326,10023,15234,495
8 +True,october,10-K,910,649,222,97
9 +True,october,10-Q,28963,11030,10684,455
10 +True,october,8-K,564,432,477,203
added results/resolver/june_october_reconciliation.json +137 −0
@@ -0,0 +1,137 @@
1 +{
2 + "sp500_ciks": 500,
3 + "mentions_in_backfilled_block": 39930,
4 + "projects_from_backfilled_block_only": 19227,
5 + "expected_june": {
6 + "mentions": 39930,
7 + "projects": 19227,
8 + "timeline_rows": 39930
9 + },
10 + "sp500_mentions_total": 70367,
11 + "sp500_projects_total": 27766,
12 + "paper_sp500_projects_october": 27766,
13 + "sp500_projects_by_run_composition": [
14 + [
15 + "june only",
16 + 15910
17 + ],
18 + [
19 + "both runs",
20 + 3317
21 + ],
22 + [
23 + "october only",
24 + 8539
25 + ]
26 + ],
27 + "sp500_mentions_by_run_form": [
28 + [
29 + "backfilled_june",
30 + "10-K",
31 + 15787,
32 + 4193
33 + ],
34 + [
35 + "backfilled_june",
36 + "10-Q",
37 + 5817,
38 + 2028
39 + ],
40 + [
41 + "backfilled_june",
42 + "8-K",
43 + 18326,
44 + 15234
45 + ],
46 + [
47 + "october",
48 + "10-K",
49 + 910,
50 + 222
51 + ],
52 + [
53 + "october",
54 + "10-Q",
55 + 28963,
56 + 10684
57 + ],
58 + [
59 + "october",
60 + "8-K",
61 + 564,
62 + 477
63 + ]
64 + ],
65 + "sp500_mentions_filing_date_le_20260525_by_run": [
66 + [
67 + "october",
68 + 28995,
69 + 11217
70 + ],
71 + [
72 + "backfilled_june",
73 + 39926,
74 + 19225
75 + ]
76 + ],
77 + "sp500_projects_filing_date_le_20260525": 27360,
78 + "backfilled_block_mentions_not_sp500": 0,
79 + "june_10q_accessions_in_block_ledger": 3710,
80 + "ledger_block_accessions_by_form": [
81 + [
82 + "10-K",
83 + 7368,
84 + 27164
85 + ],
86 + [
87 + "10-Q",
88 + 3710,
89 + 23873
90 + ],
91 + [
92 + "8-K",
93 + 103927,
94 + 103927
95 + ]
96 + ],
97 + "sp500_october_ledger_by_form": [
98 + [
99 + "10-K",
100 + 377,
101 + 1803,
102 + 912
103 + ],
104 + [
105 + "10-Q",
106 + 19399,
107 + 116231,
108 + 28996
109 + ],
110 + [
111 + "8-K",
112 + 2939,
113 + 2939,
114 + 564
115 + ]
116 + ],
117 + "sp500_block_ledger_by_form": [
118 + [
119 + "10-K",
120 + 7368,
121 + 27164,
122 + 15787
123 + ],
124 + [
125 + "10-Q",
126 + 3710,
127 + 23873,
128 + 5817
129 + ],
130 + [
131 + "8-K",
132 + 103927,
133 + 103927,
134 + 18326
135 + ]
136 + ]
137 +}
\ No newline at end of file
added results/resolver/key_branch_by_type.csv +22 −0
@@ -0,0 +1,22 @@
1 +project_type,projects,loc_keyed,token_keyed,general_keyed
2 +ma_integration,41742,22873,18922,12
3 +capex_program,10720,3638,7077,7
4 +partnership,9909,3396,6511,6
5 +geographic_expansion,7868,4895,2989,0
6 +plant_construction,7590,5773,1845,7
7 +cost_reduction,6481,2213,4275,2
8 +drug_pipeline,5360,1296,3935,130
9 +product_launch,5356,1130,4215,13
10 +rd_program,5080,954,4096,34
11 +infrastructure,4481,2801,1694,2
12 +manufacturing_expansion,4140,2614,1552,1
13 +technology_deployment,4128,1116,3004,8
14 +digital_transformation,2642,311,2329,3
15 +erp_implementation,1735,259,1478,0
16 +sustainability,1305,476,830,1
17 +supply_chain,684,269,416,0
18 +data_center,449,276,174,0
19 +energy_transition,415,243,177,0
20 +ai_initiative,376,29,345,2
21 +automation,374,70,304,0
22 +cloud_migration,264,26,237,1
added results/resolver/manifest.json +38 −0
@@ -0,0 +1,38 @@
1 +{
2 + "package": "resolver",
3 + "script": "/Users/simon-pierreboucher/firc0001_review/scripts/a03_resolver_speccurve.py",
4 + "script_sha256": "19cf2ed3aae78394a718a31caa106a20a67cb9a6450c0d30b89f6a84c7fe8ad8",
5 + "started": "2026-11-05T14:20:38",
6 + "ended": "2026-11-05T14:20:40",
7 + "wall_seconds": 2.2,
8 + "node": "m3u96b",
9 + "python": "3.12.15",
10 + "duckdb": "2.0.0",
11 + "threads": 4,
12 + "memory_limit": "24GB",
13 + "seed": 20261105,
14 + "databases": {
15 + "/Users/simon-pierreboucher/UQO_RESEARCH_DB/project-db/data/db/spid.duckdb": {
16 + "bytes": 3155701760,
17 + "mtime": "2026-10-28T12:21:11.091389"
18 + },
19 + "/Users/simon-pierreboucher/UQO_RESEARCH_DB/data/duckdb/edgar_research.duckdb": {
20 + "bytes": 73500995584,
21 + "mtime": "2026-10-27T21:34:46.297884"
22 + }
23 + },
24 + "read_only": true,
25 + "row_counts": {
26 + "top_name_tokens.csv": 50,
27 + "top_location_keys.csv": 50,
28 + "key_branch_by_type.csv": 21,
29 + "overmerge_by_type.csv": 21,
30 + "overmerge_by_bucket.csv": 5,
31 + "overmerge_ma_by_bucket.csv": 5,
32 + "example_provenance_keys.csv": 30,
33 + "spec_curve.csv": 45,
34 + "spec_curve_by_type.csv": 945,
35 + "june_october_reconciliation.csv": 9
36 + },
37 + "notes": {}
38 +}
\ No newline at end of file
added results/resolver/overmerge_by_bucket.csv +6 −0
@@ -0,0 +1,6 @@
1 +bucket,projects,pct_ge3_names,pct_ge3_partners_among_with_partner,mean_names
2 +1,72932,0.0,0.0,1.0
3 +2,20544,0.0,0.0,1.53
4 +3-5,17681,37.44,15.12,2.2
5 +6-10,6173,64.09,31.18,3.56
6 +>10,3769,86.57,52.95,9.05
added results/resolver/overmerge_by_type.csv +22 −0
@@ -0,0 +1,22 @@
1 +project_type,projects,mentions,pct_ge3_names,pct_ge2_names,pct_ge3_partners_among_with_partner,n_ge3_partners,pct_multi_all_names_distinct
2 +ma_integration,41742,130259,14.63,30.93,10.92,2289,30.24
3 +capex_program,10720,19211,6.4,19.43,4.13,189,38.86
4 +partnership,9909,17501,6.74,19.66,4.3,281,38.57
5 +geographic_expansion,7868,17713,10.18,25.23,1.88,14,35.76
6 +plant_construction,7590,18388,13.49,30.3,4.04,62,34.68
7 +cost_reduction,6481,13157,5.71,17.47,1.59,4,26.39
8 +drug_pipeline,5360,26755,27.85,46.19,11.97,238,28.07
9 +product_launch,5356,12456,12.23,27.97,3.38,23,37.89
10 +rd_program,5080,11758,11.44,27.52,3.59,32,30.05
11 +infrastructure,4481,8510,7.5,20.78,2.17,21,38.05
12 +manufacturing_expansion,4140,8825,11.26,27.08,2.17,11,34.79
13 +technology_deployment,4128,6725,4.77,15.79,1.78,17,34.48
14 +digital_transformation,2642,5249,6.25,19.3,0.0,0,25.92
15 +erp_implementation,1735,4242,3.46,17.23,1.41,1,12.86
16 +sustainability,1305,2607,6.13,15.17,1.06,2,26.77
17 +supply_chain,684,1171,3.51,15.06,1.0,1,26.96
18 +data_center,449,889,9.35,26.06,3.64,6,46.54
19 +energy_transition,415,866,10.12,24.34,1.57,2,38.41
20 +ai_initiative,376,632,4.79,16.22,0.0,0,27.83
21 +automation,374,630,2.67,13.1,4.17,1,28.04
22 +cloud_migration,264,521,5.68,17.05,0.0,0,21.88
added results/resolver/overmerge_ma_by_bucket.csv +6 −0
@@ -0,0 +1,6 @@
1 +bucket,projects,with_partner,pct_ge3_partners,pct_ge2_partners,pct_ge3_names
2 +1,22606,9999,0.0,0.0,0.0
3 +2,7558,3781,0.0,32.35,0.0
4 +3-5,7070,3855,17.3,49.0,37.52
5 +6-10,2668,1766,36.35,63.65,66.15
6 +>10,1840,1564,62.66,80.82,91.74
added results/resolver/spec_curve.csv +46 −0
@@ -0,0 +1,46 @@
1 +key,threshold,projects,mentions,single_mention_pct,gt10_mention_pct,mean_mentions,max_cluster,example_cluster_size_cheniere
2 +k_cik_type,0.45,21027,308065,20.69,29.82,14.651,399,176
3 +k_cik_type,0.6,21001,306454,20.74,29.76,14.592,399,176
4 +k_cik_type,0.7,20878,303175,20.76,29.48,14.521,399,175
5 +k_cik_type,0.8,20053,281241,21.63,28.19,14.025,375,171
6 +k_cik_type,0.9,16562,207150,25.35,24.03,12.508,343,106
7 +k_type_dropped,0.45,96749,308065,52.59,4.99,3.184,269,11
8 +k_type_dropped,0.6,96438,306454,52.61,4.96,3.178,269,11
9 +k_type_dropped,0.7,95580,303175,52.64,4.95,3.172,269,11
10 +k_type_dropped,0.8,89818,281241,53.07,4.84,3.131,269,11
11 +k_type_dropped,0.9,70174,207150,54.53,4.26,2.952,250,10
12 +k_current,0.45,121099,308065,60.23,3.11,2.544,269,8
13 +k_current,0.6,120621,306454,60.24,3.1,2.541,269,8
14 +k_current,0.7,119427,303175,60.26,3.1,2.539,269,8
15 +k_current,0.8,111079,281241,60.47,3.05,2.532,269,8
16 +k_current,0.9,83483,207150,61.35,2.91,2.481,250,7
17 +k_current_stopwords,0.45,130101,308065,59.05,2.49,2.368,209,8
18 +k_current_stopwords,0.6,129601,306454,59.06,2.48,2.365,209,8
19 +k_current_stopwords,0.7,128404,303175,59.09,2.47,2.361,209,8
20 +k_current_stopwords,0.8,119990,281241,59.21,2.4,2.344,209,8
21 +k_current_stopwords,0.9,91918,207150,59.72,2.08,2.254,206,7
22 +k_city_normalised,0.45,123246,308065,56.83,2.84,2.5,212,8
23 +k_city_normalised,0.6,122760,306454,56.83,2.83,2.496,212,8
24 +k_city_normalised,0.7,121588,303175,56.84,2.82,2.493,212,8
25 +k_city_normalised,0.8,113404,281241,56.88,2.75,2.48,212,8
26 +k_city_normalised,0.9,86347,207150,57.02,2.42,2.399,209,7
27 +k_name_only,0.45,114244,308065,56.41,3.74,2.697,230,40
28 +k_name_only,0.6,113779,306454,56.41,3.73,2.693,230,40
29 +k_name_only,0.7,112652,303175,56.43,3.73,2.691,230,39
30 +k_name_only,0.8,104680,281241,56.44,3.72,2.687,230,39
31 +k_name_only,0.9,78834,207150,56.48,3.57,2.628,226,25
32 +k_two_tokens,0.45,145697,308065,62.96,1.74,2.114,209,8
33 +k_two_tokens,0.6,145122,306454,62.97,1.73,2.112,209,8
34 +k_two_tokens,0.7,143667,303175,62.95,1.72,2.11,209,8
35 +k_two_tokens,0.8,133671,281241,62.92,1.69,2.104,209,8
36 +k_two_tokens,0.9,100490,207150,62.93,1.51,2.061,206,7
37 +k_strict,0.45,198488,308065,75.78,0.52,1.552,206,1
38 +k_strict,0.6,197673,306454,75.8,0.51,1.55,206,1
39 +k_strict,0.7,195703,303175,75.82,0.5,1.549,206,1
40 +k_strict,0.8,182052,281241,75.92,0.49,1.545,206,1
41 +k_strict,0.9,135411,207150,76.17,0.4,1.53,203,1
42 +k_mention_level,0.45,308065,308065,100.0,0.0,1.0,1,1
43 +k_mention_level,0.6,306454,306454,100.0,0.0,1.0,1,1
44 +k_mention_level,0.7,303175,303175,100.0,0.0,1.0,1,1
45 +k_mention_level,0.8,281241,281241,100.0,0.0,1.0,1,1
46 +k_mention_level,0.9,207150,207150,100.0,0.0,1.0,1,1
added results/resolver/spec_curve_by_type.csv +946 −0
@@ -0,0 +1,946 @@
1 +key,threshold,project_type,projects
2 +k_cik_type,0.45,product_launch,1100
3 +k_cik_type,0.45,geographic_expansion,1673
4 +k_cik_type,0.45,supply_chain,389
5 +k_cik_type,0.45,sustainability,537
6 +k_cik_type,0.45,ma_integration,2448
7 +k_cik_type,0.45,manufacturing_expansion,882
8 +k_cik_type,0.45,data_center,142
9 +k_cik_type,0.45,ai_initiative,209
10 +k_cik_type,0.45,drug_pipeline,301
11 +k_cik_type,0.45,capex_program,2303
12 +k_cik_type,0.45,cost_reduction,1784
13 +k_cik_type,0.45,technology_deployment,1376
14 +k_cik_type,0.45,infrastructure,1231
15 +k_cik_type,0.45,digital_transformation,904
16 +k_cik_type,0.45,automation,234
17 +k_cik_type,0.45,erp_implementation,872
18 +k_cik_type,0.45,energy_transition,116
19 +k_cik_type,0.45,partnership,2046
20 +k_cik_type,0.45,plant_construction,1292
21 +k_cik_type,0.45,rd_program,1022
22 +k_cik_type,0.45,cloud_migration,166
23 +k_cik_type,0.6,cost_reduction,1783
24 +k_cik_type,0.6,erp_implementation,872
25 +k_cik_type,0.6,capex_program,2303
26 +k_cik_type,0.6,technology_deployment,1373
27 +k_cik_type,0.6,digital_transformation,903
28 +k_cik_type,0.6,infrastructure,1229
29 +k_cik_type,0.6,energy_transition,114
30 +k_cik_type,0.6,automation,232
31 +k_cik_type,0.6,geographic_expansion,1668
32 +k_cik_type,0.6,product_launch,1099
33 +k_cik_type,0.6,sustainability,535
34 +k_cik_type,0.6,supply_chain,386
35 +k_cik_type,0.6,ma_integration,2447
36 +k_cik_type,0.6,manufacturing_expansion,880
37 +k_cik_type,0.6,data_center,142
38 +k_cik_type,0.6,drug_pipeline,301
39 +k_cik_type,0.6,ai_initiative,209
40 +k_cik_type,0.6,rd_program,1022
41 +k_cik_type,0.6,partnership,2045
42 +k_cik_type,0.6,cloud_migration,166
43 +k_cik_type,0.6,plant_construction,1292
44 +k_cik_type,0.7,geographic_expansion,1647
45 +k_cik_type,0.7,product_launch,1090
46 +k_cik_type,0.7,sustainability,522
47 +k_cik_type,0.7,supply_chain,381
48 +k_cik_type,0.7,rd_program,1014
49 +k_cik_type,0.7,partnership,2040
50 +k_cik_type,0.7,cloud_migration,166
51 +k_cik_type,0.7,plant_construction,1290
52 +k_cik_type,0.7,ma_integration,2447
53 +k_cik_type,0.7,manufacturing_expansion,873
54 +k_cik_type,0.7,data_center,142
55 +k_cik_type,0.7,drug_pipeline,301
56 +k_cik_type,0.7,ai_initiative,208
57 +k_cik_type,0.7,cost_reduction,1780
58 +k_cik_type,0.7,erp_implementation,872
59 +k_cik_type,0.7,capex_program,2301
60 +k_cik_type,0.7,technology_deployment,1360
61 +k_cik_type,0.7,digital_transformation,886
62 +k_cik_type,0.7,infrastructure,1224
63 +k_cik_type,0.7,energy_transition,113
64 +k_cik_type,0.7,automation,221
65 +k_cik_type,0.8,ma_integration,2442
66 +k_cik_type,0.8,manufacturing_expansion,829
67 +k_cik_type,0.8,ai_initiative,192
68 +k_cik_type,0.8,data_center,130
69 +k_cik_type,0.8,drug_pipeline,299
70 +k_cik_type,0.8,product_launch,1032
71 +k_cik_type,0.8,geographic_expansion,1556
72 +k_cik_type,0.8,sustainability,478
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75 +k_cik_type,0.8,rd_program,957
76 +k_cik_type,0.8,partnership,1990
77 +k_cik_type,0.8,cloud_migration,155
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82 +k_cik_type,0.8,infrastructure,1155
83 +k_cik_type,0.8,erp_implementation,852
84 +k_cik_type,0.8,energy_transition,108
85 +k_cik_type,0.8,digital_transformation,825
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87 +k_cik_type,0.9,product_launch,818
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90 +k_cik_type,0.9,ma_integration,2440
91 +k_cik_type,0.9,manufacturing_expansion,648
92 +k_cik_type,0.9,drug_pipeline,292
93 +k_cik_type,0.9,data_center,93
94 +k_cik_type,0.9,ai_initiative,118
95 +k_cik_type,0.9,rd_program,652
96 +k_cik_type,0.9,partnership,1737
97 +k_cik_type,0.9,cloud_migration,96
98 +k_cik_type,0.9,plant_construction,1157
99 +k_cik_type,0.9,cost_reduction,1566
100 +k_cik_type,0.9,capex_program,2227
101 +k_cik_type,0.9,erp_implementation,701
102 +k_cik_type,0.9,technology_deployment,909
103 +k_cik_type,0.9,digital_transformation,473
104 +k_cik_type,0.9,infrastructure,930
105 +k_cik_type,0.9,energy_transition,87
106 +k_cik_type,0.9,automation,60
107 +k_type_dropped,0.45,product_launch,4115
108 +k_type_dropped,0.45,geographic_expansion,5770
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110 +k_type_dropped,0.45,supply_chain,450
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113 +k_type_dropped,0.45,drug_pipeline,4665
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118 +k_type_dropped,0.45,partnership,7417
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121 +k_type_dropped,0.45,technology_deployment,2837
122 +k_type_dropped,0.45,capex_program,8149
123 +k_type_dropped,0.45,digital_transformation,1938
124 +k_type_dropped,0.45,erp_implementation,1539
125 +k_type_dropped,0.45,infrastructure,2979
126 +k_type_dropped,0.45,automation,260
127 +k_type_dropped,0.45,energy_transition,252
128 +k_type_dropped,0.6,product_launch,4106
129 +k_type_dropped,0.6,geographic_expansion,5736
130 +k_type_dropped,0.6,supply_chain,447
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138 +k_type_dropped,0.6,technology_deployment,2822
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205 +k_type_dropped,0.9,digital_transformation,679
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