In the following discussion, it is assumed that the evaluation methods employed are based on the least-squares approach, e.g., as embodied in code GMAP [49]. The estimated USU contributions, when significant, need to be introduced as components of augmented covariance matrices associated with all the input experimental data. It has been shown by Capote and Neudecker [78] that when more than a one-dimensional quantity is to be evaluated (e.g., energy-dependent cross sections), the evaluated mean values, as well as the derived covariances, will be altered by the use of augmented covariance matrices that include the USU contributions. This is intuitively evident because a change in the inclusive **data** covariance matrix changes the effective weighings of the input **data** points and this will alter the evaluated mean values. Therefore, a-priori determined USU contribution (e.g., as done for a one-dimensional quantity 252 Cf(sf) ν tot , see Sect. V A below) cannot be added a-posteriori to evaluated uncertainties while keeping the originally evaluated mean values. Instead, the evaluations need to be repeated including these augmented covariance matrices that contain an USU component in the experimental covariance matrix.