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Addition of measured stand density to both models significantly improved the R2 to, respectively, 0.76 and 0.70 for TSV and V.
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Both age-period and age-cohort models significantly improved the fit over the age only and age-drift models.
Interestingly, adding ScysC in both univariate and multivariate eGFR-based models significantly improved the overall performance of the model (Table 5).> -wrap-foot> Additiof of Cystatin C significantly improved both univariate and multivariate mGFR based models.
Both the age period (AP) and age cohort (AC) models significantly improved the fit over the age only and age drift models.
The validation data sets confirm the training results: combining the three individual models significantly improved classification in 4 out of 6 data sets, while no significant improvement was obtained in 2 out of 6 data sets.
The results of this study show that use of the regularized constitutive models significantly improved the accuracy of response predictions.
Thus the PM and MSW models significantly improved the accuracy against the original SW model and can be used to estimate ET and Es under the film-mulching condition.
Further, the combination of the different WVC models significantly improved the F-measure.
Different from the ineffectiveness of individual-level social capital variables, the introduction of community social capital variables in the different models significantly improved the fit of the models.
Our models significantly improved the fitting performance of the postseismic time series and the prediction performance of the evolution of postseismic deformation.
The relative merits of competing models evaluated by Bayes factors indicated that all mixed models significantly improved model fit compared to the homogeneous models, especially the JC model (Table 4).
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