Gaussian regression score ($\dot{\ell }=h_{\theta }(X)\epsilon /{\sigma }^2$)

For a regression function $g_{\theta }$, let $h_{\theta }={\partial }_{\theta }{g}_{\theta }$. With independent centered normal distribution error of known variance ${\sigma }^2$, the parametric score function is $h_{\theta }(X)\epsilon /{\sigma }^2$. It has zero conditional expectation given the covariate, so unknown covariate-density nuisance directions do not remove any of its Fisher information.