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The correlation coefficient values for the models were high (>0.99).
With the exception of stand density, the coefficients of determination for the final developed models were high ranging from 0.79 to 0.86.
The functional data in these first level models were high pass filtered with a cut-off of 128 seconds and corrected for autocorrelation by an AR(1) model [69].
Additionally, the correlations of predicted values on hot days (heat index ≥ 90°F) among these models were high (Table 2, Figure 3).
Validation statistics for all models were high with a mean area under the receiver operator curve (AUC) above 0.97 and mean correlations above 0.85 for all models.> -wrap-foot> In the New World, CL is predicted to occur primarily within the Amazon basin and other areas of rainforest.
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The models were high-ranking individuals that monopolized the box early on and discovered by trial and error how to open it.
In addition, the sensitivities of all classification models were higher than 87%, indicating that the pre-trained machine learning models are sensitive when identifying older people who are at high risk of falling.
R2 values of the established models were higher than 0.844, indicating that models were adequate to predict HMF content.
Among them, the probabilities of 19 models were higher than 0.005.
The functional data in these first level models were high-pass filtered with a cut-off of 128 s and corrected for autocorrelation by an AR(1) model (Friston et al. 2002).
The functional data in these first-level models were high-pass filtered with a cutoff of 128 s and corrected for autocorrelation by an AR(1) model [Friston et al., 2002].
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