we performed 1,000 software injections, split into eight groups of 125 signals. The strain $h_0$ of each group was $\pm 5\%, \pm 10\%, \pm 15\%, \textup{and} \pm 20\%$ of the semi-analytic , respectively. A software injection was considered validated if it returned a value of $2\mathcal{F}$ greater than or equal to the loudest outlier in its 0.1-Hz subband, thus maintaining the original granularity. For each 1-Hz band, these 1,000 injections thus produced eight points on a detection efficiency curve. We then used a least-squares method to produce a sigmoid fit to the data points, and from this curve determined a true 95% upper limit, defined as the value of $h_0$ at which the fitting curve intersected 95% efficiency. Figure  \[ExamplePlot\] shows such a plot for a sample band. In cases where the 95% point was extrapolated (as opposed to interpolated) from the eight points, and in cases where the uncertainty on the 95% point was greater than 5%, a new set of eight points were generated using the 95% point as the initial $h_{0}$ estimate and a 95% point was determined from the combined sets (e.g., a curve was fit to 16 points after one rerun, 24 points after two 