spb/firc0001_uqo

1% ============================================================================2\section{Descriptive Patterns}\label{sec:results}3% ============================================================================45All numbers in this section are produced by \texttt{code/export\_stats.py} and \texttt{code/make\_tables.py} from the merged databases at revision \texttt{2324b58}; none is typed by hand. They describe \emph{disclosed} initiatives as detected by the current pipeline and resolved by the current rule. They are not estimates of real-world project counts, and they have not yet been corrected for the extraction and linkage errors whose measurement is described in Section~\ref{sec:validation}.67\subsection{From sections to projects}89The extraction ledger contains 1{,}123{,}098 sections. The model returned at least one project for 168{,}230 of them (15.0\%), yielding 308{,}065 mentions---0.27 per section on average and 1.83 per productive section. The density differs sharply by form and by item (Table~\ref{tab:items}): the MD\&A items of the 10-K and 10-Q produce 39{,}099 and 99{,}305 mentions respectively, Item~1 (Business) of the 10-K 30{,}492, and the 10-Q financial-statement notes 30{,}159, whereas risk-factor and market-risk items produce almost none despite their length. The 8-K body yields 74{,}594 mentions from 400{,}601 filings. Resolution reduces 308{,}065 mentions to 121{,}099 projects (2.54 mentions per project); 60.2\% of projects rest on a single mention, 17.0\% on two, and 8.2\% on more than five (Table~\ref{tab:status}).1011\begin{table}[H]12\centering\small13\caption{Sections submitted and mentions obtained, by form and item}\label{tab:items}14\begin{threeparttable}15\input{tables/tab_items}16\begin{tablenotes}\footnotesize17\item 8-K filings (400{,}601 sections, 74{,}594 mentions) are a single \texttt{full\_body} section each and are omitted from the table. \texttt{full\_body}/\texttt{full\_document} rows are filings in which no Item heading was recognized.18\end{tablenotes}19\end{threeparttable}20\end{table}2122\subsection{Composition by category, sector and status}2324Table~\ref{tab:types} and Figure~\ref{fig:types} show the distribution over the 21 categories. M\&A and post-merger integration dominate (34.5\% of projects), followed by capital-expenditure programs (8.9\%), strategic partnerships (8.2\%), geographic expansion (6.5\%) and plant or site construction (6.3\%). The technology-related categories that motivated the first SPID release---data centers, AI initiatives, cloud migration, automation---are small in count (between 264 and 449 projects each) but carry high disclosed amounts (median 128--166~M USD for data centers and AI initiatives). Two readings of this composition are possible and the data do not discriminate between them: either acquisitions are genuinely the most frequently disclosed discrete initiative in SEC filings, which is plausible given that every material acquisition triggers an 8-K and recurring 10-Q notes; or the extraction contract, which asks for ``concrete initiatives'', is more easily satisfied by acquisitions (named counterparty, date, price) than by internal programs. Disentangling the two is a question for the human-annotation study.2526\begin{table}[H]27\centering\small28\caption{Resolved projects by category}\label{tab:types}29\begin{threeparttable}30\adjustbox{max width=\textwidth}{\input{tables/tab_types}}31\begin{tablenotes}\footnotesize32\item ``With amount'' counts projects for which at least one mention carries a USD figure; ``median amount'' is the median across those projects of the largest amount cited (see Section~\ref{sec:amounts} on why amounts are never summed).33\end{tablenotes}34\end{threeparttable}35\end{table}3637\begin{figure}[H]38\centering39\includegraphics[width=0.95\textwidth]{figures/fig_types.pdf}40\caption{Resolved projects by taxonomy category (121{,}099 projects).}\label{fig:types}41\end{figure}4243By sector (Table~\ref{tab:sectors}), health care (23{,}679 projects, 429 issuers) and industrials (17{,}890) contribute the most projects; utilities, materials, energy and real estate have the most projects per issuer (63--75), and financials the fewest (32), consistent with the intuition that asset-heavy, regulated or resource-extracting firms narrate discrete physical programs while banks describe fewer discrete initiatives. The list of the most ``productive'' issuers (Table~\ref{tab:top_issuers} in the appendix)---uranium and gold miners, airlines, a bitcoin miner, an oilfield-services company---illustrates a second mechanism: issuers whose business \emph{consists} of projects (mines, routes, wells, data-center sites) and whose filings enumerate them one by one.4445\begin{table}[H]46\centering\small47\caption{Issuers and projects by sector}\label{tab:sectors}48\begin{threeparttable}49\adjustbox{max width=\textwidth}{\input{tables/tab_sectors}}50\begin{tablenotes}\footnotesize51\item Sector labels come from the iShares holdings file (Russell proxy) or, for S\&P~500-only issuers, from the Wikipedia GICS table. ``Issuers in universe'' includes issuers without filings.52\end{tablenotes}53\end{threeparttable}54\end{table}5556The latest status recorded for a project is \texttt{in\_progress} for 41.6\%, \texttt{completed} for 41.0\%, \texttt{planned} for 15.1\% and \texttt{mentioned} for 2.3\% (Table~\ref{tab:status}). Because status is taken from the most recent mention, it is a snapshot of the last thing the issuer said, not the terminal fate of the project: a project that was announced, mentioned once as in progress and then silently abandoned is recorded as in progress. The span between the earliest and latest year cited \emph{in the text} is zero for 55.5\% of projects with known years and exceeds five years for 8.5\%; the brief's distinction between announcement, filing, expected start, observed start, expected and realized completion, and cancellation is not yet implemented as separate dated events (Section~\ref{sec:resolution}).5758\begin{table}[H]59\centering\small60\caption{Status, number of mentions and cited time span of resolved projects}\label{tab:status}61\adjustbox{max width=\textwidth}{\input{tables/tab_status}}62\end{table}6364\subsection{Amounts, confidence and time}\label{sec:amounts_desc}6566A USD amount is attached to 120{,}240 mentions (39.0\%) and to 56{,}500 projects (46.7\%). Table~\ref{tab:amounts} reports the distribution of the largest amount cited per project: median 128.5~M USD, interquartile range 23--500~M, and a long right tail (P99 of 12.5~B USD). The sum of these maxima across projects is 55.9~trillion USD, a number we report only to make the point that it must \emph{not} be interpreted: it adds transaction values, capex budgets, contract values, financing amounts and estimated costs across projects and years, it double counts initiatives that the resolver split, and it treats a stated budget as if it were spent. Section~\ref{sec:amounts} explains how amounts are stored and what an \texttt{amount\_type} field would need to add.6768\begin{table}[H]69\centering\small70\caption{Distribution of disclosed amounts and of projects per issuer}\label{tab:amounts}71\adjustbox{max width=\textwidth}{\input{tables/tab_amounts}}72\end{table}7374The self-reported confidence of mentions is concentrated at the top of the scale (Figure~\ref{fig:confidence}; Table~\ref{tab:confidence} in the appendix): 35.2\% of mentions have confidence 1.0, 32.7\% have 0.9, and only 1.6\% fall at or below 0.6. Mentions below 0.45 are discarded at extraction time. The distribution says more about the model's reporting habits than about accuracy; whether a confidence of 0.7 corresponds to a lower precision than a confidence of 1.0 is precisely the calibration question that only labeled data can answer.7576\begin{figure}[H]77\centering78\includegraphics[width=0.85\textwidth]{figures/fig_confidence.pdf}79\caption{Self-reported extraction confidence of the 308{,}065 mentions (rounded to one decimal; mentions below 0.45 are not stored).}\label{fig:confidence}80\end{figure}8182Over time (Figure~\ref{fig:mentions_year}; Table~\ref{tab:coverage_year}), mentions rise from 11{,}908 in 2010 to 23{,}220 in 2025, almost in proportion to the number of issuers observed (1{,}224 to 2{,}280 with at least one mention), while the number of projects first observed in a year is flat at 6{,}200--8{,}200. The rise is therefore mostly a coverage effect of the snapshot universe, not evidence of an increase in strategic activity; the flat count of new projects per year combined with a growing issuer base implies a declining number of new projects per issuer, which may reflect the lower disclosure intensity of the smaller firms that enter the panel later, or the resolver's tendency to attach later mentions to existing keys. Both explanations are testable with the panel.8384\begin{figure}[H]85\centering86\includegraphics[width=\textwidth]{figures/fig_mentions_year.pdf}87\caption{Project mentions and projects first observed, by filing year (2026 covers January to October 27).}\label{fig:mentions_year}88\end{figure}8990\subsection{A provenance example}9192Table~\ref{tab:provenance} follows one resolved project through its mentions: the Corpus Christi liquefaction expansion of Cheniere Energy (ticker LNG), typed \texttt{plant\_construction}, with eight mentions in 8-K and 10-K filings between April 2014 and March 2022 and a largest cited amount of 11.5~B USD. Each row is traceable: the accession number identifies the document on EDGAR, the section identifier locates the passage in the parsed corpus, and the snippet is the model's one-sentence description, which the contract requires to be grounded in the passage. The example also exposes the resolver's coarseness: the project key for this issuer and type is the location token ``corpus christi'', so the Stage~3 expansion and the earlier trains share one project identifier although Cheniere describes them as distinct phases. A reviewer can see this immediately because the mentions and their snippets are preserved; a user who consumes only the \texttt{projects} table cannot.9394\begin{table}[H]95\centering\footnotesize96\caption{Provenance of one resolved project: Cheniere Energy, Corpus Christi liquefaction (project \texttt{3e2705ba\ldots})}\label{tab:provenance}97\begin{threeparttable}98\adjustbox{max width=\textwidth}{\input{tables/tab_provenance}}99\begin{tablenotes}\footnotesize100\item Snippets are the first 170 characters of the model-generated description stored in \texttt{project\_timeline.snippet}; the full source section is retrievable through \texttt{project\_mentions.section\_id}. Amounts are shown only for mentions that cite one.101\end{tablenotes}102\end{threeparttable}103\end{table}104105\subsection{Knowledge graph and research-ready exports}106107The resolved projects are projected into a graph of 183{,}472 nodes (2{,}492 companies, 121{,}099 projects, 14{,}508 locations, 8{,}551 technologies, 36{,}822 partners) and 204{,}456 edges (\texttt{owns}, \texttt{located\_in}, \texttt{uses}; Table~\ref{tab:graph} in the appendix); partner nodes are currently created without edges, a known gap. Each project also has a 384-dimensional sentence embedding of its name, type, description and technologies, which supports semantic search through the companion web application. Alongside the project layer, the corpus layer publishes the firm-year and firm-quarter panels of thematic factor scores (32{,}852 and 128{,}043 rows) used in earlier UQO work on AI disclosure, now covering the full universe.108