\begin{equation}
P(x) = \frac{1}{\beta} \exp\p[{\frac{x-\alpha}{\beta}
-\exp\p({\frac{x-\alpha}{\beta}})
- }]
+ }] \label{eq:sawsim:gumbel-x}
\end{equation}
is given by $\mu=\alpha-\gamma\beta$, and the variance is
$\sigma^2=\frac{1}{6}\pi^2\beta^2$, where $\gamma=0.57721566\ldots$ is
the Euler-Mascheroni constant. Selecting $\beta=1/a=k_BT/\dd x$,
$\alpha=-\beta\ln(\kappa\beta/kv)$, and $F=x$ we have
+\nomenclature{$\mu$}{The mean of a distribution (e.g. the Gumbel
+ distribution, \cref{eq:sawsim:gumbel-x}).}
\begin{align}
P(F)
&= \frac{1}{\beta} \exp\p[{\frac{F+\beta\ln(\kappa\beta/kv)}{\beta}