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| \appendix |
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| \providecommand{\ms}[2]{\ensuremath{#1{\mkern1mu}_{\scriptscriptstyle \pm #2}}} |
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| \section*{Appendix Contents} |
| \addcontentsline{toc}{section}{Appendix Contents} |
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|
| \begin{center} |
| \begin{tabular}{@{}p{0.82\textwidth}r@{}} |
| \textbf{A\quad Evaluation Contract Specifications} & \pageref{sec:app_contract} \\ |
| \quad A.1\enspace Matching Rule Definitions & \pageref{sec:app_contract_matching} \\ |
| \quad A.2\enspace Task-Form Contract Parameters & \pageref{sec:app_contract_params} \\ |
| \quad A.3\enspace Evaluation Scope Definitions & \pageref{sec:app_contract_scope} \\[4pt] |
| \textbf{B\quad Controlled Check Details} & \pageref{sec:app_checks} \\ |
| \quad B.1\enspace Fixed-Output Check: Full Sweep & \pageref{sec:app_checks_output} \\ |
| \quad B.2\enspace Fixed-Feature Check: Selection Summary & \pageref{sec:app_checks_feature} \\ |
| \quad B.3\enspace Selection Regret Under Matching Rules & \pageref{sec:app_checks_regret} \\ |
| \quad B.4\enspace Additional Value Tables & \pageref{sec:app_checks_values} \\[4pt] |
| \textbf{C\quad Comparator Eligibility Notes} & \pageref{sec:comparator_audit} \\[4pt] |
| \textbf{D\quad Seeded Audits} & \pageref{sec:app_seeded_audits} \\ |
| \quad D.1\enspace Seed Robustness Summary & \pageref{sec:app_seed_robustness} \\[4pt] |
| \textbf{E\quad Lightweight Head and Adaptation Details} & \pageref{sec:app_heads} \\[4pt] |
| \textbf{F\quad Limitations} & \pageref{sec:limitations} \\[4pt] |
| \textbf{G\quad Reproducibility and Evaluation Artifacts} & \pageref{sec:repro_compute_impact} \\ |
| \end{tabular} |
| \end{center} |
|
|
| \noindent\textit{Retention rule.} |
| Appendix tables are retained when they add contract parameters, controlled-check arithmetic, |
| task-specific non-main metrics, seed summaries, eligibility checks, or protocol details. |
| Full task matrices and reference-summary tables that repeat the main result tables are not repeated here. |
|
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| \clearpage |
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| |
| |
| |
| \section{Evaluation Contract Specifications} |
| \label{sec:app_contract} |
|
|
| |
| \subsection{Matching Rule Definitions} |
| \label{sec:app_contract_matching} |
|
|
| The three matching rules used across occupancy task forms are defined as follows. |
|
|
| \noindent\textbf{Exact matching.} |
| A predicted unit-time pair $(i,t) \in \widehat{P}_\tau$ is counted as a true positive if and only if the same pair appears in the observed fire set $P = \{(i,t): y_{i,t}=1\}$. |
| This is the strictest rule and yields the lowest $F_1$ for any fixed output. |
|
|
| \noindent\textbf{Tolerated matching.} |
| A predicted pair $(i,t)$ is counted as correct if there exists an observed pair $(i',t') \in P$ such that $\|i - i'\|_\infty \le k$ and $|t - t'| \le \Delta t$, where $k$ is the spatial tolerance in grid cells and $\Delta t$ is the temporal tolerance in forecast steps. |
| Both parameters are fixed as part of the evaluation contract $\mathcal{C}$ before scoring. |
|
|
| \noindent\textbf{Union matching.} |
| A predicted pair is counted as a true positive if it satisfies either exact or tolerated matching. |
| The resulting union-$F_1$ provides an upper bound on decision performance under the chosen tolerance. |
|
|
| \noindent\textbf{Fixed parameter values.} |
| For occupancy, the spatial tolerance is $k=8$ grid cells. |
| The temporal tolerance is $\Delta t=3$ forecast steps for union matching and $\Delta t=0$ for spatial-only tolerance. |
| The threshold $\tau$ is selected on validation strict-$F_1$ before test scoring. |
| For fire spread, the spatial tolerance is $k=4$ grid cells, $\Delta t=0$, and the threshold is selected on validation spatial $F_1$. |
|
|
| \noindent Table~\ref{tab:app_matching_rule_params} records the fixed matching-rule parameters. |
|
|
| \begin{table}[h] |
| \centering |
| \small |
| \setlength{\tabcolsep}{10pt} |
| \renewcommand{\arraystretch}{1.2} |
| \caption{Matching-rule values used in the evaluation contracts.} |
| \label{tab:app_matching_rule_params} |
| \begin{tabular}{lll} |
| \toprule |
| \textbf{Parameter} & \textbf{Occupancy} & \textbf{Fire spread} \\ |
| \midrule |
| \(k\) & 8 cells & 4 cells \\ |
| \(\Delta t\) & 3 for union; 0 spatial-only & 0 \\ |
| \(\tau\) & val. strict \(F_1\) & val. spatial \(F_1\) \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| |
| \subsection{Task-Form Contract Parameters} |
| \label{sec:app_contract_params} |
|
|
| Table~\ref{tab:app_contract_params_full} lists fixed scoring values not shown in the main contract map. |
|
|
| \begin{table}[h] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3.5pt} |
| \renewcommand{\arraystretch}{1.2} |
| \caption{Fixed scoring values used by each task-form contract.} |
| \label{tab:app_contract_params_full} |
| \begin{adjustbox}{max width=\textwidth} |
| \begin{tabular}{llll} |
| \toprule |
| \textbf{\(\mathcal{T}\)} & \textbf{Scoring} & \textbf{Validation} & \textbf{\(\Omega\)} \\ |
| \midrule |
| Occupancy & \(k=8,\Delta t=3\); exact/tol./union \(F_1\) & val. strict \(F_1\) & global; top-5/10/20\% fire-prone \\ |
| Fire spread & \(k=4,\Delta t=0\); exact/spatial \(F_1\), AP & val. spatial \(F_1\) & spread-region cells \\ |
| Final burned area & log-RMSE, log-MAE, Spearman \(\rho\) & val. log-RMSE & test events \\ |
| Analog retrieval & nDCG@10; retrieved-event log error & val. nDCG@10 & test events \\ |
| Smoke PM\(_{2.5}\) & RMSE, MAE, Pearson \(r\); exceedance 35 & val. RMSE & test stations \\ |
| Extreme heat & RMSE-C, MAE-C, exceedance \(F_1\) & val. threshold 27/30/33\(^{\circ}\)C & heat-region stations \\ |
| \bottomrule |
| \end{tabular} |
| \end{adjustbox} |
| \end{table} |
|
|
| |
| \subsection{Evaluation Scope Definitions} |
| \label{sec:app_contract_scope} |
|
|
| \noindent\textbf{Global scope.} |
| Evaluation covers all spatial units in the domain, including fire-inactive regions. |
| This scope can mask model differences on fire-relevant locations because inactive cells inflate true-negative counts. |
|
|
| \noindent\textbf{Fire-prone scope.} |
| Evaluation is restricted to grid cells in the top-$k$\% of historical fire activity. |
| We report results for top-5\%, top-10\%, and top-20\% cutoffs. |
| The cutoff thresholds are derived from the training period and held fixed at test time. |
|
|
| \noindent\textbf{Spread region scope.} |
| For fire spread tasks, evaluation is restricted to the predicted and observed burned raster patches. |
| Only cells within the union of $\widehat{B}$ and $B$ contribute to metric computation. |
|
|
| \noindent\textbf{Fixed scope sizes.} |
| The global scope contains 8,085,000 test cells. |
| The fire-prone top-5\%, top-10\%, and top-20\% scopes contain 404,280, 808,560, and 1,617,000 test cells, respectively. |
| The spread-region scope is event-specific and uses the union of $\widehat{B}$ and $B$. |
|
|
| \begin{table}[h] |
| \centering |
| \small |
| \setlength{\tabcolsep}{8pt} |
| \renewcommand{\arraystretch}{1.2} |
| \caption{Scope values used in the evaluation contracts.} |
| \label{tab:app_scope_params} |
| \begin{tabular}{lcc} |
| \toprule |
| \textbf{\(\Omega\)} & \textbf{Definition} & \textbf{Units} \\ |
| \midrule |
| Global & full domain & 8,085,000 test cells \\ |
| Fire-prone top-5\% & top 5\% by training-period fire frequency & 404,280 test cells \\ |
| Fire-prone top-10\% & top 10\% by training-period fire frequency & 808,560 test cells \\ |
| Fire-prone top-20\% & top 20\% by training-period fire frequency & 1,617,000 test cells \\ |
| Spread region & union of \(\widehat{B}\) and \(B\) & event-specific cells \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \clearpage |
|
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| |
| |
| |
| \section{Controlled Check Details} |
| \label{sec:app_checks} |
|
|
|
|
| \begin{figure}[t] |
| \centering |
| \includegraphics[width=\textwidth]{figures/fig_fireprone_contract_progression_compact.pdf} |
| \caption{ |
| \textbf{Matching-rule sensitivity in fire-prone occupancy (RQ1).} |
| Each row holds the score field \(S\), label field \(Y\), threshold, and \(\Omega\) fixed, and changes only \(\Lambda\). |
| Legend: \textcolor[HTML]{17375E}{$\blacksquare$} strict \(F_1\), |
| \textcolor[HTML]{4F8DCC}{$\blacksquare$} added \(F_1\) from spatial tolerance, |
| \textcolor[HTML]{BFD7F0}{$\blacksquare$} added \(F_1\) from union matching, |
| red outline \ourfm, and dashed line original weather FMs vs.\ added baselines. |
| The horizontal axis is \(F_1\) in percent. |
| } |
| \label{fig:fireprone_contract_progression} |
| \end{figure} |
|
|
| |
| \subsection{Fixed-Output Check: Full Sweep} |
| \label{sec:app_checks_output} |
|
|
| The fixed-output check holds the score field $S$ and label field $Y$ fixed and varies only $\Lambda$. |
| Table~\ref{tab:fireprone_contract_progression} reports the full global and fire-prone sweep for all retained backbones. |
| The same table is the numeric counterpart to Figure~\ref{fig:fireprone_contract_progression}. |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{4pt} |
| \caption{Occupancy \(F_1\) scores across global and fire-prone scopes. Global uses the full validation/test domain; top-\(k\) rows use train-defined fire-prone masks from historical fire frequency. Values are percentages from the same validation-selected strict threshold. Tolerance is spatial-only; union adds temporal and spatial matching. \(\Delta\) is union minus strict. Cells report five-seed mean with std in small type.} |
| \label{tab:fireprone_contract_progression} |
| \begin{tabular}{@{}llcccc@{}} |
| \toprule |
| Backbone & \(\Omega\) & Strict \(F_1\uparrow\) & Tol.\ \(F_1\uparrow\) & Union \(F_1\uparrow\) & \(\Delta\) \(\uparrow\) \\ |
| \midrule |
| \ourfm & global & \ms{0.4546}{0.1412} & \ms{29.7484}{1.2868} & \ms{59.0656}{2.7372} & \ms{58.6109}{2.6945} \\ |
| & top 5\% & \ms{3.5604}{0.8809} & \ms{39.2617}{1.4011} & \ms{72.8280}{2.5784} & \ms{69.2676}{1.9960} \\ |
| & top 10\% & \ms{3.5575}{0.8799} & \ms{39.1665}{1.3906} & \ms{72.5204}{2.5670} & \ms{68.9629}{1.9888} \\ |
| & top 20\% & \ms{3.5300}{0.8700} & \ms{38.2849}{1.2952} & \ms{69.7228}{2.4664} & \ms{66.1928}{1.9273} \\ |
| \addlinespace[1pt] |
| Prithvi-WxC & global & \ms{0.0552}{0.0039} & \ms{7.1649}{0.6557} & \ms{20.1853}{1.8299} & \ms{20.1301}{1.8297} \\ |
| & top 5\% & \ms{1.4119}{1.1635} & \ms{19.2636}{4.5019} & \ms{42.5793}{4.5495} & \ms{41.1674}{3.4846} \\ |
| & top 10\% & \ms{1.2376}{1.3201} & \ms{14.8780}{8.4429} & \ms{32.6913}{13.2085} & \ms{31.4536}{11.9053} \\ |
| & top 20\% & \ms{1.1520}{1.3770} & \ms{13.1512}{9.4556} & \ms{28.1319}{15.2866} & \ms{26.9800}{13.9224} \\ |
| \addlinespace[1pt] |
| Aurora & global & \ms{0.0656}{0.0094} & \ms{8.5009}{1.9594} & \ms{23.1037}{4.9418} & \ms{23.0382}{4.9325} \\ |
| & top 5\% & \ms{0.9859}{0.9299} & \ms{15.1337}{6.0821} & \ms{35.4834}{11.0192} & \ms{34.4975}{10.3728} \\ |
| & top 10\% & \ms{0.7790}{1.0453} & \ms{12.7381}{6.5558} & \ms{30.5305}{10.8842} & \ms{29.7515}{9.8656} \\ |
| & top 20\% & \ms{0.6655}{1.1043} & \ms{10.5304}{7.4309} & \ms{24.9444}{12.5844} & \ms{24.2790}{11.4943} \\ |
| \addlinespace[1pt] |
| ClimaX & global & \ms{0.3480}{0.0754} & \ms{29.7535}{3.6073} & \ms{60.1506}{7.5865} & \ms{59.8026}{7.5454} \\ |
| & top 5\% & \ms{1.2937}{0.1086} & \ms{34.5791}{2.3772} & \ms{69.2186}{5.7215} & \ms{67.9249}{5.7263} \\ |
| & top 10\% & \ms{1.2522}{0.1602} & \ms{34.3341}{2.2852} & \ms{68.5713}{5.5377} & \ms{67.3191}{5.5538} \\ |
| & top 20\% & \ms{1.0287}{0.2686} & \ms{30.2140}{4.2857} & \ms{60.0650}{7.5674} & \ms{59.0363}{7.5891} \\ |
| \addlinespace[1pt] |
| StormCast & global & \ms{0.0626}{0.0119} & \ms{8.1951}{2.1895} & \ms{22.3817}{5.4294} & \ms{22.3191}{5.4178} \\ |
| & top 5\% & \ms{0.9573}{0.8011} & \ms{15.3219}{5.5337} & \ms{36.1857}{9.7331} & \ms{35.2284}{9.1816} \\ |
| & top 10\% & \ms{0.7284}{0.9280} & \ms{12.6669}{6.3290} & \ms{30.4748}{10.6527} & \ms{29.7464}{9.7494} \\ |
| & top 20\% & \ms{0.5795}{0.9104} & \ms{10.4157}{7.3437} & \ms{24.6598}{12.3973} & \ms{24.0803}{11.4988} \\ |
| \addlinespace[1pt] |
| DLWP & global & \ms{0.1693}{0.0419} & \ms{14.9148}{3.2446} & \ms{28.1901}{6.9658} & \ms{28.0208}{6.9257} \\ |
| & top 5\% & \ms{1.8054}{0.4835} & \ms{31.7231}{3.2923} & \ms{55.4596}{5.2920} & \ms{53.6542}{5.4752} \\ |
| & top 10\% & \ms{1.6110}{0.5999} & \ms{27.6581}{5.9216} & \ms{47.1269}{8.0111} & \ms{45.5158}{7.7927} \\ |
| & top 20\% & \ms{1.5248}{0.8987} & \ms{20.9403}{4.7971} & \ms{34.9301}{7.8471} & \ms{33.4054}{7.8760} \\ |
| \addlinespace[1pt] |
| FCN & global & \ms{0.2829}{0.0839} & \ms{19.5061}{3.3412} & \ms{40.0604}{9.3701} & \ms{39.7775}{9.3423} \\ |
| & top 5\% & \ms{1.6231}{0.5064} & \ms{29.3769}{2.7626} & \ms{54.3033}{7.4089} & \ms{52.6801}{7.4389} \\ |
| & top 10\% & \ms{1.1777}{0.5118} & \ms{22.4217}{3.9803} & \ms{43.4510}{9.2513} & \ms{42.2734}{9.0251} \\ |
| & top 20\% & \ms{0.9962}{0.4315} & \ms{16.9792}{3.9371} & \ms{34.0859}{8.2616} & \ms{33.0897}{7.9275} \\ |
| \addlinespace[1pt] |
| FengWu & global & \ms{0.2613}{0.0757} & \ms{12.0050}{6.0239} & \ms{24.1022}{13.6293} & \ms{23.8410}{13.5736} \\ |
| & top 5\% & \ms{1.5695}{0.3592} & \ms{16.2763}{3.7024} & \ms{30.1055}{5.0103} & \ms{28.5360}{4.7696} \\ |
| & top 10\% & \ms{1.2427}{0.5333} & \ms{12.9503}{5.6052} & \ms{24.1854}{8.6854} & \ms{22.9427}{8.1863} \\ |
| & top 20\% & \ms{1.1192}{0.5023} & \ms{11.9508}{5.0745} & \ms{22.7860}{7.9115} & \ms{21.6668}{7.4438} \\ |
| \addlinespace[1pt] |
| FuXi & global & \ms{0.3774}{0.1212} & \ms{21.0323}{4.8211} & \ms{37.2888}{9.4470} & \ms{36.9114}{9.4327} \\ |
| & top 5\% & \ms{2.0307}{0.6800} & \ms{31.8944}{4.7331} & \ms{53.9308}{8.3822} & \ms{51.9001}{8.6878} \\ |
| & top 10\% & \ms{1.6542}{0.7316} & \ms{24.0128}{5.7784} & \ms{40.2140}{9.9307} & \ms{38.5597}{9.7744} \\ |
| & top 20\% & \ms{1.3646}{0.6773} & \ms{21.9548}{5.8601} & \ms{36.7314}{10.0289} & \ms{35.3668}{9.9223} \\ |
| \addlinespace[1pt] |
| Pangu-Weather & global & \ms{0.2755}{0.1089} & \ms{17.0909}{4.0477} & \ms{35.6386}{9.0327} & \ms{35.3630}{9.0774} \\ |
| & top 5\% & \ms{1.3656}{0.3064} & \ms{22.2222}{6.8613} & \ms{43.4234}{13.2383} & \ms{42.0578}{13.0599} \\ |
| & top 10\% & \ms{1.0931}{0.3535} & \ms{18.9337}{5.9329} & \ms{38.5325}{11.7221} & \ms{37.4394}{11.5261} \\ |
| & top 20\% & \ms{0.8844}{0.3601} & \ms{17.0172}{5.4859} & \ms{34.5688}{10.2932} & \ms{33.6844}{10.1334} \\ |
| \addlinespace[1pt] |
| AlphaEarth & global & \ms{2.0606}{0.4404} & \ms{29.4476}{6.0064} & \ms{37.4286}{9.9458} & \ms{35.3679}{10.0271} \\ |
| & top 5\% & \ms{6.9133}{0.8450} & \ms{42.8790}{4.6087} & \ms{51.7449}{8.7321} & \ms{44.8315}{9.0763} \\ |
| & top 10\% & \ms{6.6366}{0.9901} & \ms{41.8981}{5.9454} & \ms{50.5712}{10.0057} & \ms{43.9346}{9.9156} \\ |
| & top 20\% & \ms{6.1908}{1.1330} & \ms{38.8325}{7.4966} & \ms{46.3833}{12.1697} & \ms{40.1925}{11.6788} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| |
| \subsection{Fixed-Feature Check: Selection Summary} |
| \label{sec:app_checks_feature} |
|
|
| The paper appendix keeps the fixed-feature result at the selection-summary level. |
| The full per-head rows are retained in the supplementary CSV files and are not repeated as a manuscript table because many degenerate heads produce identical zero decision scores. |
| The supplementary selection rows report the decision-score loss after changing only the head-selection metric. |
|
|
| |
| \subsection{Selection Regret Under Matching Rules} |
| \label{sec:app_checks_regret} |
|
|
| The fixed-feature check trains the same head family $\mathcal{H}$ on a fixed feature source and changes only the selection metric. |
| Table~\ref{tab:appendix_selection_regret_tolerance} reports the same selection comparison under exact, tolerated, and union matching. |
| Here, \(h_R\) is selected by PR-AUC and \(h_D\) is selected by the decision metric. |
| The reported regret is \(D(h_D)-D(h_R)\). |
| Exact zero entries mean the two selectors give the same decision score for all five seeds. |
|
|
| \begin{table*}[!t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{4pt} |
| \caption{Selection-regret values under exact, tolerated, and union matching. Values are percentage-point regret from selecting \(h_R\) by PR-AUC instead of \(h_D\) by the decision metric. Rows report mean with small std over five seeds; \(0.0000\) denotes exact zero regret.} |
| \label{tab:appendix_selection_regret_tolerance} |
| \begin{adjustbox}{max width=\textwidth} |
| \begin{tabular}{llccc} |
| \toprule |
| \textbf{Feature} & \textbf{\(\Omega\)} & \textbf{Exact regret} & \textbf{Tolerated regret} & \textbf{Union regret} \\ |
| \midrule |
| \ourfm & global & 0.0000 & \ms{8.7830}{9.6705} & \ms{8.7830}{9.6705} \\ |
| \ourfm & fire-prone & 0.0000 & \ms{3.4027}{3.2045} & \ms{3.4027}{3.2045} \\ |
| Prithvi-WxC & global & 0.0000 & 0.0000 & 0.0000 \\ |
| Prithvi-WxC & fire-prone & 0.0000 & 0.0000 & 0.0000 \\ |
| Aurora & global & \ms{0.0200}{0.0267} & \ms{9.8520}{12.9878} & \ms{9.8520}{12.9878} \\ |
| Aurora & fire-prone & \ms{0.8203}{1.8341} & \ms{14.3919}{32.1219} & \ms{14.3919}{32.1219} \\ |
| ClimaX & global & \ms{0.0003}{0.0004} & \ms{0.1296}{0.1775} & \ms{0.1296}{0.1775} \\ |
| ClimaX & fire-prone & 0.0000 & 0.0000 & 0.0000 \\ |
| StormCast & global & 0.0000 & 0.0000 & 0.0000 \\ |
| StormCast & fire-prone & 0.0000 & 0.0000 & 0.0000 \\ |
| DLWP & global & 0.0000 & 0.0000 & 0.0000 \\ |
| DLWP & fire-prone & \ms{0.0770}{0.1100} & \ms{4.3266}{4.3323} & \ms{4.3266}{4.3323} \\ |
| FCN & global & 0.0000 & 0.0000 & 0.0000 \\ |
| FCN & fire-prone & \ms{0.0006}{0.0013} & \ms{1.1680}{1.9872} & \ms{1.1680}{1.9872} \\ |
| FengWu & global & 0.0000 & 0.0000 & 0.0000 \\ |
| FengWu & fire-prone & \ms{0.0691}{0.1191} & \ms{0.5222}{0.6239} & \ms{0.5222}{0.6239} \\ |
| FuXi & global & 0.0000 & 0.0000 & 0.0000 \\ |
| FuXi & fire-prone & 0.0000 & \ms{0.1084}{0.1729} & \ms{0.1084}{0.1729} \\ |
| Pangu-Weather & global & 0.0000 & 0.0000 & 0.0000 \\ |
| Pangu-Weather & fire-prone & \ms{0.0728}{0.1179} & \ms{0.1849}{0.3263} & \ms{0.1849}{0.3263} \\ |
| AlphaEarth & global & 0.0000 & \ms{17.2217}{8.8492} & \ms{17.2217}{8.8492} \\ |
| AlphaEarth & fire-prone & 0.0000 & \ms{3.8804}{5.9483} & \ms{3.8804}{5.9483} \\ |
| \bottomrule |
| \end{tabular} |
| \end{adjustbox} |
| \end{table*} |
|
|
|
|
| |
| \subsection{Additional Value Tables} |
| \label{sec:app_checks_values} |
|
|
| Table~\ref{tab:app_occupancy_ppr_scope} |
| reports the predicted-positive rate behind the occupancy \(F_1\) sweep. |
|
|
| \begin{table*}[t] |
| \centering |
| \small |
| \setlength{\tabcolsep}{4pt} |
| \renewcommand{\arraystretch}{1.18} |
| \caption{For fixed occupancy \(\mathcal{T}\), this table reports predicted-positive rate. |
| Values are percentages under the same validation-selected strict threshold. |
| Scopes \(\Omega\) are fixed before test scoring; cells report five-seed mean with std in small type.} |
| \label{tab:app_occupancy_ppr_scope} |
| \begin{tabular}{lcccc} |
| \toprule |
| \textbf{Backbone} & \textbf{\(\Omega=\)global} & \textbf{\(\Omega=\)top 5\%} & \textbf{\(\Omega=\)top 10\%} & \textbf{\(\Omega=\)top 20\%} \\ |
| \midrule |
| \ourfm & \ms{1.6808}{0.3684} & \ms{3.0619}{1.0925} & \ms{1.5310}{0.5463} & \ms{0.7655}{0.2732} \\ |
| Prithvi-WxC & \ms{61.9711}{30.9101} & \ms{57.4117}{47.8987} & \ms{58.4565}{51.0897} & \ms{58.9788}{52.6991} \\ |
| Aurora & \ms{55.5849}{19.7524} & \ms{57.2238}{35.3400} & \ms{68.7942}{37.6958} & \ms{67.2891}{38.3991} \\ |
| ClimaX & \ms{5.6763}{3.9261} & \ms{24.0091}{9.2816} & \ms{11.8450}{4.5067} & \ms{5.7442}{4.1341} \\ |
| StormCast & \ms{60.6507}{17.4895} & \ms{57.6017}{35.2921} & \ms{68.0766}{37.3899} & \ms{67.8397}{39.2410} \\ |
| DLWP & \ms{4.3221}{1.5619} & \ms{9.4001}{5.0807} & \ms{4.9700}{3.6849} & \ms{1.9198}{1.4678} \\ |
| FCN & \ms{1.5202}{1.3446} & \ms{4.7856}{2.9409} & \ms{2.7257}{1.6353} & \ms{0.8368}{0.2358} \\ |
| FengWu & \ms{0.4277}{0.4830} & \ms{0.6004}{0.3041} & \ms{0.2609}{0.1935} & \ms{0.1501}{0.1206} \\ |
| FuXi & \ms{0.4505}{0.2773} & \ms{2.9315}{2.6392} & \ms{0.5197}{0.6074} & \ms{0.3621}{0.4346} \\ |
| Pangu-Weather & \ms{1.0801}{1.1308} & \ms{2.0549}{2.1893} & \ms{1.4029}{1.4739} & \ms{1.0103}{1.1084} \\ |
| AlphaEarth & \ms{0.0691}{0.0499} & \ms{0.2826}{0.1497} & \ms{0.1524}{0.0770} & \ms{0.0656}{0.0414} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| Tables~\ref{tab:app_spread_ap_by_scope}--\ref{tab:app_heat_event_pr} |
| report additional values that are not repeated in the main tables. |
| Each table fixes the task \(\mathcal{T}\) and reports either a different \(\Omega\), metric, or event subset. |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{For fixed spread \(\mathcal{T}\) and strict \(\Lambda\), this table reports AP under three \(\Omega\) scopes: full test, top-5\% train-fire area, and top-10\% train-fire area. Values are percentages; cells report mean with small std.} |
| \label{tab:app_spread_ap_by_scope} |
| \begin{tabular}{lccc} |
| \toprule |
| Backbone & full \(\Omega\) AP & top-5\% \(\Omega\) AP & top-10\% \(\Omega\) AP \\ |
| \midrule |
| \ourfm & \ms{30.0197}{1.5651} & \ms{40.7452}{2.0542} & \ms{37.4096}{1.8731} \\ |
| Prithvi-WxC & \ms{4.8319}{0.1731} & \ms{12.6086}{0.4468} & \ms{8.7051}{0.1889} \\ |
| Aurora & \ms{17.7723}{0.4293} & \ms{30.3106}{0.9404} & \ms{26.4732}{0.6932} \\ |
| ClimaX & \ms{11.1726}{0.2337} & \ms{25.7871}{1.2896} & \ms{19.9977}{1.2217} \\ |
| StormCast & \ms{8.1147}{1.1569} & \ms{18.5461}{1.1727} & \ms{14.1286}{1.2956} \\ |
| DLWP & \ms{9.2142}{2.6587} & \ms{19.3346}{2.3922} & \ms{14.9788}{2.6696} \\ |
| FCN & \ms{6.6774}{1.3001} & \ms{16.7396}{3.2955} & \ms{11.9308}{2.3881} \\ |
| FengWu & \ms{11.0046}{2.7092} & \ms{21.1506}{1.2163} & \ms{17.0113}{1.5778} \\ |
| FuXi & \ms{13.5507}{0.3840} & \ms{22.5434}{0.4100} & \ms{19.1964}{0.3943} \\ |
| Pangu-Weather & \ms{10.6250}{1.4643} & \ms{19.8294}{1.3044} & \ms{15.8013}{1.1602} \\ |
| AlphaEarth & \ms{12.2847}{1.3562} & \ms{22.8692}{0.4915} & \ms{18.2992}{1.2110} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{For fixed final-area \(\mathcal{T}\) and \(\Omega\), this table reports median log error and acre-scale errors in addition to the main log-RMSE/log-MAE/Spearman metrics. Cells report mean with small std.} |
| \label{tab:app_burned_area_median_acre} |
| \begin{tabular}{lccc} |
| \toprule |
| Backbone & log median AE & acre median AE & acre MAPE \\ |
| \midrule |
| \ourfm & \ms{1.0235}{0.0982} & \ms{4504.0692}{459.0483} & \ms{1.4525}{0.0254} \\ |
| Prithvi-WxC & \ms{1.2184}{0.2107} & \ms{5375.8770}{788.7906} & \ms{1.9517}{0.2875} \\ |
| Aurora & \ms{1.4547}{0.0301} & \ms{9904.9483}{457.4260} & \ms{6.8728}{3.0026} \\ |
| ClimaX & \ms{1.6841}{0.1818} & \ms{18130.4820}{3248.3873} & \ms{8.2373}{2.8540} \\ |
| StormCast & \ms{1.4522}{0.1519} & \ms{11155.7881}{2020.8656} & \ms{4.6142}{1.1500} \\ |
| DLWP & \ms{1.0952}{0.1306} & \ms{4406.9315}{303.0944} & \ms{1.7357}{0.3625} \\ |
| FCN & \ms{1.1688}{0.1139} & \ms{5166.9993}{213.0333} & \ms{2.0800}{0.4004} \\ |
| FengWu & \ms{1.1589}{0.1772} & \ms{5137.2822}{628.7543} & \ms{2.0944}{0.4545} \\ |
| FuXi & \ms{1.1855}{0.0612} & \ms{5697.7117}{796.8785} & \ms{2.4411}{0.5567} \\ |
| Pangu-Weather & \ms{1.1221}{0.1470} & \ms{5092.3621}{483.8243} & \ms{1.9571}{0.3113} \\ |
| AlphaEarth & \ms{1.7459}{0.6057} & \ms{15110.7573}{7106.3417} & \ms{9.7398}{2.7425} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{For fixed retrieval \(\mathcal{T}\) and \(\Omega\), this table reports nDCG@5, best log gap, and rank \(\rho\) in addition to the main nDCG@10/log-error metrics. Cells report mean with small std.} |
| \label{tab:app_analog_rank_depth} |
| \begin{tabular}{lccc} |
| \toprule |
| Backbone & nDCG@5 & best log gap & rank $\rho$ \\ |
| \midrule |
| \ourfm & \ms{0.5175}{0.0445} & \ms{0.1868}{0.0285} & \ms{0.6019}{0.1460} \\ |
| Prithvi-WxC & \ms{0.3591}{0.0107} & \ms{0.2151}{0.0594} & \ms{0.1514}{0.1489} \\ |
| Aurora & \ms{0.4423}{0.0210} & \ms{0.1551}{0.0437} & \ms{0.2162}{0.1856} \\ |
| ClimaX & \ms{0.4151}{0.0293} & \ms{0.2129}{0.0653} & \ms{0.1587}{0.2831} \\ |
| StormCast & \ms{0.3960}{0.0240} & \ms{0.1714}{0.0310} & \ms{0.1258}{0.1625} \\ |
| DLWP & \ms{0.3795}{0.0274} & \ms{0.1944}{0.0807} & \ms{-0.3865}{0.2802} \\ |
| FCN & \ms{0.4250}{0.0112} & \ms{0.1856}{0.0846} & \ms{-0.1357}{0.2571} \\ |
| FengWu & \ms{0.4228}{0.0310} & \ms{0.1870}{0.0858} & \ms{-0.1926}{0.2194} \\ |
| FuXi & \ms{0.4544}{0.0356} & \ms{0.2171}{0.0806} & \ms{-0.1367}{0.2885} \\ |
| Pangu-Weather & \ms{0.3988}{0.0506} & \ms{0.1901}{0.0838} & \ms{-0.1970}{0.2216} \\ |
| AlphaEarth & \ms{0.5276}{0.0531} & \ms{0.1782}{0.0454} & \ms{0.4639}{0.2802} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{For fixed smoke \(\mathcal{T}\) and station \(\Omega\), this table reports RMSE, MAE, and 90th-percentile absolute error on test rows with observed PM$_{2.5}\ge35$; std uses a row bootstrap over those rows. Cells report mean with small std.} |
| \label{tab:app_smoke_high_event} |
| \begin{tabular}{lccc} |
| \toprule |
| Backbone & high-smoke RMSE & high-smoke MAE & high-smoke 90th AE \\ |
| \midrule |
| \ourfm & \ms{47.4870}{0.6346} & \ms{34.3954}{0.7654} & \ms{65.6213}{3.8778} \\ |
| Prithvi-WxC & \ms{57.2224}{1.7268} & \ms{47.3871}{0.3153} & \ms{74.9666}{3.2381} \\ |
| Aurora & \ms{57.2752}{1.7248} & \ms{47.4368}{0.3149} & \ms{75.0755}{3.1074} \\ |
| ClimaX & \ms{57.2828}{1.7239} & \ms{47.4407}{0.3140} & \ms{75.1012}{3.0777} \\ |
| StormCast & \ms{56.6512}{1.7517} & \ms{46.7914}{0.3281} & \ms{74.0794}{3.4707} \\ |
| DLWP & \ms{57.0075}{1.7359} & \ms{47.1971}{0.3198} & \ms{74.4936}{3.3826} \\ |
| FCN & \ms{57.0582}{1.7339} & \ms{47.2401}{0.3187} & \ms{74.6431}{3.1982} \\ |
| FengWu & \ms{57.0158}{1.7357} & \ms{47.1957}{0.3194} & \ms{74.5652}{3.2871} \\ |
| FuXi & \ms{56.9622}{1.7371} & \ms{47.1508}{0.3201} & \ms{74.3278}{3.4435} \\ |
| Pangu-Weather & \ms{57.1282}{1.7307} & \ms{47.3050}{0.3170} & \ms{74.6830}{3.2375} \\ |
| AlphaEarth & \ms{48.0665}{0.7904} & \ms{35.6088}{0.7341} & \ms{66.7613}{3.9235} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| \begin{table*}[t] |
| \centering |
| \scriptsize |
| \setlength{\tabcolsep}{3pt} |
| \caption{For fixed heat \(\mathcal{T}\) and heat-region \(\Omega\), this table reports precision and recall for the exceedance label used by the main \(F_1\). Cells report mean with small std.} |
| \label{tab:app_heat_event_pr} |
| \begin{tabular}{lcc} |
| \toprule |
| Backbone & precision & recall \\ |
| \midrule |
| \ourfm & \ms{0.9767}{0.0117} & \ms{0.9330}{0.0299} \\ |
| Prithvi-WxC & \ms{0.8260}{0.0030} & \ms{0.9173}{0.0033} \\ |
| Aurora & \ms{0.5920}{0.0347} & \ms{0.0517}{0.0020} \\ |
| ClimaX & \ms{0.7397}{0.0099} & \ms{0.7994}{0.0051} \\ |
| StormCast & \ms{0.8840}{0.0237} & \ms{0.9320}{0.0165} \\ |
| DLWP & \ms{0.9429}{0.0085} & \ms{0.8899}{0.0167} \\ |
| FCN & \ms{0.9408}{0.0097} & \ms{0.9111}{0.0127} \\ |
| FengWu & \ms{0.3808}{0.2719} & \ms{0.0266}{0.0267} \\ |
| FuXi & \ms{0.3262}{0.1262} & \ms{0.1810}{0.0481} \\ |
| Pangu-Weather & \ms{0.1159}{0.0743} & \ms{0.0112}{0.0032} \\ |
| AlphaEarth & \ms{0.9824}{0.0040} & \ms{0.9278}{0.0178} \\ |
| \bottomrule |
| \end{tabular} |
| \end{table*} |
|
|
| \clearpage |
|
|
| |
| |
| |
| \section{Comparator Eligibility Notes} |
| \label{sec:comparator_audit} |
|
|
| All numeric comparator rows in Tables~\ref{tab:primary_results} and~\ref{tab:supporting_results} |
| are included only after the task form, metric, matching rule, scope, and head family are fixed. |
| The appendix does not repeat those full matrices. |
| The key eligibility rule is simple: reported rows satisfy the same contract as the row block in which they appear, while excluded rows are excluded because their representation or output form does not satisfy that contract. |
|
|
| \noindent\textbf{Reading rule.} |
| Exact-only, tolerated, union, ranking, retrieval, and regression scores answer different questions. |
| The fixed-contract reading is therefore to compare entries only within one row block and not to average across task forms. |
|
|
| \clearpage |
|
|
| |
| |
| |
| \section{Seeded Audits} |
| \label{sec:app_seeded_audits} |
|
|
| \subsection{Seed Robustness Summary} |
| \label{sec:app_seed_robustness} |
|
|
| Table~\ref{tab:app_seed_robustness} summarizes stochastic checks used to support the reported mean-with-std convention. |
| It is not a replacement for the main fixed-contract result tables. |
|
|
| \begin{table}[h] |
| \centering |
| \small |
| \setlength{\tabcolsep}{5pt} |
| \renewcommand{\arraystretch}{1.2} |
| \caption{Seed summaries for stochastic checks. Values report mean with small std over completed seeds.} |
| \label{tab:app_seed_robustness} |
| \begin{adjustbox}{max width=\textwidth} |
| \begin{tabular}{p{0.28\textwidth}cllp{0.18\textwidth}} |
| \toprule |
| \textbf{\(\mathcal{T}\) check} & \textbf{Seeds} & \textbf{Primary value} & \textbf{Other value(s)} & \textbf{Reading} \\ |
| \midrule |
| Final burned area & |
| 5 & log-RMSE \ms{1.1657}{0.0126} & |
| log-MAE \ms{1.0423}{0.0081}; Spear.\ \ms{0.6298}{0.0338} & |
| stable across seeds \\ |
| Smoke PM\(_{2.5}\) & |
| 5 & RMSE \ms{4.4646}{0.0060} & |
| MAE \ms{2.4108}{0.0016}; \(r\) \ms{0.6368}{0.0013} & |
| stable at table precision \\ |
| Extreme heat & |
| 5 & RMSE-C \ms{0.2179}{0.0043} & |
| MAE-C \ms{0.1787}{0.0018}; exceed.\ \(F_1\) \ms{0.9541}{0.0164} & |
| stable across seeds \\ |
| Fire spread & |
| 5 & exact \(F_1\) \ms{37.6700}{0.9800} & |
| spatial \(F_1\) \ms{80.9700}{2.0200}; AP \ms{30.0900}{1.2500} & |
| stable across seeds \\ |
| Aurora paired-head check & |
| 5 & fire-prone score diff.\ \ms{6.3500}{13.2800} & |
| PR-AUC and union choices differ in 2/5 seeds & |
| variable across seeds \\ |
| \bottomrule |
| \end{tabular} |
| \end{adjustbox} |
| \end{table} |
|
|
| \clearpage |
|
|
| |
| |
| |
| \section{Lightweight Head and Adaptation Details} |
| \label{sec:app_heads} |
|
|
| All frozen-transfer comparisons use the same five lightweight head architectures applied |
| on top of the frozen backbone representations. |
| Table~\ref{tab:app_head_architectures} summarises each head family, its architecture, |
| approximate parameter count, and the adaptation procedure used. |
|
|
| \begin{table}[h] |
| \centering |
| \small |
| \setlength{\tabcolsep}{5pt} |
| \renewcommand{\arraystretch}{1.3} |
| \caption{Lightweight head architectures used in the fixed-contract transfer comparisons. |
| All heads are trained from random initialisation on the frozen backbone features. |
| Parameter counts are approximate and depend on the feature dimensionality of each backbone.} |
| \label{tab:app_head_architectures} |
| \begin{tabular}{p{0.15\textwidth}p{0.30\textwidth}p{0.12\textwidth}p{0.33\textwidth}} |
| \toprule |
| \textbf{$\mathcal{A}$ head} & \textbf{Architecture} & \textbf{Approx.\ params} & \textbf{Notes} \\ |
| \midrule |
| Constant prior & |
| Outputs a fixed bias vector, ignoring input features. & |
| Output dimension only & |
| Provides a degenerate baseline; selected when backbone features carry no useful signal. \\ |
| Linear probe & |
| Single linear layer mapping backbone features to output. No nonlinearity. & |
| $d\times c + c$ & |
| Standard frozen-representation baseline. \\ |
| Pixel MLP & |
| Two-layer MLP applied independently per spatial unit. & |
| $d\times h + h\times c$ & |
| Captures per-pixel nonlinearity; ignores spatial context. \\ |
| Shallow adapter & |
| Two-layer MLP with a spatial context window; uses $3\times3$ convolution before the linear output. & |
| $9dh + hc$ & |
| Balances local spatial context with parameter efficiency. \\ |
| Wide adapter & |
| Shallow adapter with wider hidden dimension. & |
| $9dH + Hc$ & |
| Higher capacity variant; can overfit on small fire-event sets. \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| \noindent\textbf{Training protocol.} |
| Each occupancy head-control run uses seeds $\{1,7,42,99,123\}$, the five heads listed above, and the fixed variants identity, erode-r1, and close-r1. |
| The spread U-Net reference is trained for 4 epochs. |
| The threshold $\tau$ is selected on the validation split by maximising union-$F_1$ (for occupancy) or spatial $F_1$ (for spread) and held fixed at test time. |
| Morphology parameters (spatial tolerance $k$, temporal tolerance $\Delta t$) are fixed as part of the evaluation contract and are not tuned after validation. |
|
|
| \noindent\textbf{Head selection procedure.} |
| For each (feature source, scope, seed) tuple, all five heads are trained independently. |
| The PR-AUC-based selector picks $h_R = \arg\max_{h \in \mathcal{H}} R(h)$ on the validation set; |
| the decision-based selector picks $h_D = \arg\max_{h \in \mathcal{H}} D(h)$ on the same set. |
| The selection regret $\delta = D(h_D) - D(h_R) \ge 0$ is computed on the held-out test set. |
|
|
| \clearpage |
|
|
| |
| |
| |
| \section{Limitations} |
| \label{sec:limitations} |
|
|
| The conclusions apply to the task forms, scopes, evaluation rules, and comparator eligibility decisions used in this study. |
| The evaluation covers selected wildfire decision tasks and supporting retrieval and regression task forms. |
| Comparator eligibility is fixed before metric values are interpreted. |
| This eligibility rule keeps each comparison within one task-form contract. |
| It also leaves some model and task pairs outside the evaluated comparison set by design. |
|
|
| The transfer comparison uses frozen backbones with lightweight heads. |
| The results therefore describe frozen-backbone transfer under the allowed head families in each contract. |
| Full fine-tuning, alternative adaptation procedures, and broader head families are outside the evaluated scope. |
| The task-specific reference baselines serve as empirical anchors for same-contract comparison. |
| \ourfm is a regional wildfire reference for the reported California fixed-contract experiments. |
|
|
|
|
| The supporting retrieval and regression checks bound the primary spatial decision claim. |
| They provide task-form evidence rather than a single score across all wildfire-related prediction tasks. |
| The analysis focuses on the reported metric families, matching rules, and fixed comparison choices. |
| Operational response rules, intervention costs, and deployment policies are part of wildfire early-warning use contexts~\cite{goldammer1999early,pickell2017early,farahmand2020fdeo}. |
| They are outside the scope of this evaluation study and are not inferred from the reported scores. |
|
|
| \clearpage |
|
|
| |
| |
| |
| \section{Reproducibility and Evaluation Artifacts} |
| \label{sec:repro_compute_impact} |
| \subsection{External Assets and Terms of Use} |
| \label{sec:external_assets_terms} |
|
|
| We use external datasets and model assets only for research evaluation. |
| Access to each asset follows the original provider's portal, license, or terms of use; this submission does not imply that all assets are openly redistributable. |
| We do not redistribute raw external datasets, provider-hosted embeddings, or third-party model weights. |
| Table~\ref{tab:external_assets_licenses} records the source and terms-of-use status used to interpret reproducibility. |
|
|
| \begin{table}[h] |
| \centering |
| \small |
| \setlength{\tabcolsep}{4pt} |
| \renewcommand{\arraystretch}{1.18} |
| \caption{External assets used by the study and their source or terms-of-use status.} |
| \label{tab:external_assets_licenses} |
| \begin{tabular}{p{0.25\textwidth}p{0.34\textwidth}p{0.34\textwidth}} |
| \toprule |
| \textbf{Asset family} & \textbf{Use in this study} & \textbf{Source and terms-of-use note} \\ |
| \midrule |
| NOAA HRRR fields~\cite{noaa_hrrr_ncei,noaa_hrrr_emc} |
| & Dynamic weather inputs for \ourfm and transfer tasks. |
| & NOAA provider terms and citation requirements apply. \\ |
| NASA FIRMS~\cite{nasa_firms} |
| & Active-fire occupancy supervision. |
| & NASA Earthdata/FIRMS access terms and citation requirements apply. \\ |
| LANDFIRE and WRC layers~\cite{landfire_fbfm40,landfire_canopy_cover,usfs_wrc_housing_density} |
| & Static fuel, canopy, and exposure context. |
| & Original geospatial-product provider terms and citations apply. \\ |
| LandScan~\cite{ornl_landscan_2024} |
| & Static population context. |
| & ORNL/LandScan source-specific access terms apply; raw data are not redistributed. \\ |
| WFIGS and MTBS~\cite{nifc_wfigs_perimeters,mtbs_usgs_2025} |
| & Event-level resources for burned-area and analog tasks. |
| & Original incident/perimeter-product provider terms and citations apply. \\ |
| External Earth-FM baselines~\cite{schmude2024prithviwxc,bodnar2025aurora,nguyen2023climax,pathak2024stormcast,weyn2020dlwp,pathak2022fourcastnet,chen2023fengwu,chen2023fuxi,bi2023panguweather,brown2025alphaearth} |
| & Frozen comparator representations or task-model baselines. |
| & Original model-provider licenses and access terms apply; third-party weights are not redistributed. \\ |
| \bottomrule |
| \end{tabular} |
| \end{table} |
|
|
| This note supports the NeurIPS checklist and identifies the files that support the reported claims. |
| This file statement does not imply full raw-data release. |
| The main claims can be checked from the manuscript contracts, metric |
| definitions, and per-head result files, even if full raw-data release is |
| delayed or limited. Sections~3 and~4 specify the contract components used by |
| the main claims: task definition, split logic, label space, tolerance |
| parameters, scope definitions, threshold or operating-point rules, and |
| lightweight-head set. |
|
|
| The supplementary source includes the check scripts, per-head and per-seed |
| CSV result files, and \LaTeX{} result tables for the expanded check and matching-rule support. |
| These files expose exact \(F_1\), |
| tolerated \(F_1\), union-\(F_1\), PR-AUC, per-head selection, |
| top-1 agreement, and selection-regret arithmetic. The manuscript also includes |
| full figure and table reproduction values in result tables and appendix tables. |
| These files provide a runnable check of the |
| selection-regret arithmetic and the table-construction logic from fixed |
| per-head rows. The seeded occupancy check uses seeds |
| $\{1,7,42,99,123\}$, and the spread task-specific U-Net check uses repeated seeds; reported error bars are standard deviations over the completed |
| seeded runs. Full raw wildfire inputs and large feature arrays are not |
| released at submission because redistribution and storage constraints require a |
| separate review. |
|
|
| For stochastic results, the paper reports mean with standard deviation over repeated seeds. |
| For fixed-output or fixed-feature controls, the table uses one fixed output or feature set; the changed item is the matching rule or selection metric. |
|
|
| The reported experiments use two resource classes on a shared Slurm-managed |
| cluster. Tabular retrieval/regression checks and same-feature head controls run |
| on CPU workers with 4 to 8 cores, 24 to 64~GB host memory, and 2 to 4~hour wall-clock |
| limits. Spread U-Net training and threshold calibration run on single-GPU jobs |
| with one B200 GPU, 8 CPU cores, 96~GB host memory, and a 4~hour wall-clock |
| limit. The seed/check waves reported in the appendix correspond to roughly |
| 78 CPU job-hours and 12 GPU job-hours of scheduled wall-clock budget; |
| exploratory runs are not included in the reported compute accounting. |
|
|
| The raw-data limitation is separate from the selection-regret files. |
| The supplementary source is sufficient to inspect the selection-regret arithmetic and reproduce the reported tables. |
| Full end-to-end recomputation from raw wildfire inputs is not included at submission because redistribution review is still required. |
| The broader impact is evaluation-facing rather than operational. |
| Better reading of wildfire transfer evidence can reduce overconfident benchmark claims, while misread transfer results could still encourage inappropriate reliance on models with low decision scores. |
| For that reason, the paper keeps its claims wildfire-centered, decision-task |
| specific, and explicitly separate from any predictive deployment |
| recommendation. |
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|
