Bias on \(r\), obtained by subtracting the mean of the model 00 (the now negligible algorithmic bias, see
Table 00) from each of the 6 complex foreground models, for the case \(A_L\) = 0.1, assuming no decorrelation or linear decorrelation for the Gaussian model. For this Gaussian foreground case, we report a bias based on the absolute value of the sample variance on the mean for \(\simeq 500\) sims, which acknowledges statistical limitations exist even for closed-loop tests calibrated by MC sims.