\begin{table}[p]
\centering\scriptsize
\begin{threeparttable}
\caption{Monte Carlo, exp contam design, $p = 1$, $N = 1,048,576$: scaled gap to $\widehat\theta_N$, scaled RMSE and 95\% coverage by local sample size $m$.}\label{tab:mc_exp_contam_p1}
\begin{tabular}{@{}lrrr@{}}
\toprule
Estimator & \multicolumn{3}{c}{$m = 16$, $K = 65,536$} \\
 & gap & RMSE & cov \\
\midrule
(1,0) plain average & 101.734 & 17.256 & 0.000 \\
(1,1) average + 1 Newton & 11.018 & 95.547 & 0.000 \\
(1,2) average + 2 Newton & 0.129 & 84.659 & 0.000 \\
(2,0) half-block jackknife & 12.900 & 71.658 & 0.000 \\
(2,1) half-block + 1 Newton & 0.179 & 84.707 & 0.000 \\
(2,2) half-block + 2 Newton & 3.51e-5 & 84.530 & -- \\
(3,0) quarter-block & 21.466 & 63.100 & -- \\
(3,1) quarter-block + 1 Newton & 0.493 & 85.020 & 0.000 \\
analytic Cox--Snell + 1 Newton & 1.432 & 85.961 & 0.000 \\
CSL, 1 round & 1.39e-13 & 84.530 & -- \\
CSL, 2 rounds & 1.35e-13 & 84.530 & -- \\
AEE (Lin--Xi) & 120.265 & 204.789 & -- \\
(2,1) stale Hessian & 1.806 & 82.728 & -- \\
centralized MLE & 0 & 84.530 & 0.000 \\
\bottomrule
\end{tabular}
\begin{tablenotes}\footnotesize
\item 1000 paired replications per column. gap $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \widehat\theta_N\rVert$; RMSE $=$ root mean square of $\sqrt N\,\lVert\tilde\theta - \theta_0\rVert$; cov $=$ empirical coverage of nominal 95\% Wald intervals (observed information at the estimate; `--' when not computed). Monte Carlo standard errors of the gaps and RMSEs are in \texttt{results/processed/summary.csv}; coverage standard error $\approx 0.007$.
\item Fraction of workers with all local fits valid (orders 2 / 3): $m=16$: 1.000 / 1.000.
\end{tablenotes}
\end{threeparttable}
\end{table}