Thorough uncertainty analysis is beyond the scope of this report. However, we can use straightforward Monte Carlo propagation (500 samples) of the reported energy uncertainties to obtain that major component of uncertainty. The results are included in Table between parentheses, and refer to the least significant digits. For example, using the icMRCI+Q guiding potential and quadratic model function yields standard deviations (in cm -1 ) of 6.6, 0.21, 0.0026, 0.001, 0.0002 and 0.03e-6 for the constants e, exe, eye, Be, e and De, respectively. The propagated uncertainties mostly increase with the order of the fitting polynomial, as expected from the corresponding reduction in noise averaging. In this case, we do not know the "correct" results because we do not have a highresolution grid of QMC data. From the F2 test we expect that higher-order polynomials will give more accurate results. (This expectation is supported by the increasing similarity among the alternatively guided potentials for CO as the polynomial degree is increased-see Supporting Information.) Greater precision is associated with a smaller variation of results from different guiding potentials. Considering the values of e in Table , the spreads are 33 cm -1 from a linear model function, 1.3 cm -1 from a quadratic, and 1.4 cm -1 from a cubic. For exe, the spreads are 1.02, 0.14 and 0.24 cm -1 from linear, quadratic and cubic, respectively. The results appear reasonably converged with the quadratic model function. Since the highest-level guiding potential is from icMRCI+Q, and it shows small and near-linear differences (Fig. ), we might favor the results from that guiding potential and the quadratic model function (boldface in Table ). Note that a cubic function fits the four data points exactly, which means that the statistical noise in the data will not be canceled at all (by least-squares averaging).