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Using e + e − collision data corresponding to a total integrated luminosity of 12.9 fb −1 collected with the BESIII detector at the BEPCII collider, the exclusive Born cross sections and the effective form factors of the reaction e + e − → Ξ − Ξ+ are measured via the single baryon-tag method at 23 center-of-mass energies between 3.510 and 4.843 GeV. Evidence for the decay ψ(3770) → Ξ − Ξ+ is observed with a significance of 4.5σ by analyzing the measured cross sections together with earlier BESIII results. For the other charmonium(-like) states ψ(4040), ψ(4160), Y (4230), Y (4360), ψ(4415), and Y (4660), no significant signal of their decay to Ξ − Ξ+ is found. For these states, upper limits of the products of the branching fraction and the electronic partial width at the 90% confidence level are provided.

Assessing the accuracy of predictive models is critical because predictive models have been increasingly used across various disciplines and predictive accuracy determines the quality of resultant predictions. Pearson product-moment correlation coefficient (r) and the coefficient of determination (r2) are among the most widely used measures for assessing predictive models for numerical data, although they are argued to be biased, insufficient and misleading. In this study, geometrical graphs were used to illustrate what were used in the calculation of r and r2 and simulations were used to demonstrate the behaviour of r and r2 and to compare three accuracy measures under various scenarios. Relevant confusions about r and r2, has been clarified. The calculation of r and r2 is not based on the differences between the predicted and observed values. The existing error measures suffer various limitations and are unable to tell the accuracy. Variance explained by predictive models based on cross-validation (VEcv) is free of these limitations and is a reliable accuracy measure. Legates and McCabe’s efficiency (E1) is also an alternative accuracy measure. The r and r2 do not measure the accuracy and are incorrect accuracy measures. The existing error measures suffer limitations. VEcv and E1 are recommended for assessing the accuracy. The applications of these accuracy measures would encourage accuracy-improved predictive models to be developed to generate predictions for evidence-informed decision-making.

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