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The GARP CVA models showed low accuracy when tested against confirmed locations of invasives due to the large modeling scale.

Three of our six MaxEnt models showed low suitability for large occupied regions of interior India (false omissions), and thus were judged to fail the native range inclusion screen (Table 2).

Both MTLn3 CXCR7 and MTLn3 CXCR4-CXCR7 tumor models showed low numbers of intravasated cancer cells.

However, these models showed low accuracy levels for etiological diagnosis (areas under ROC curve for virus and mycoplasma pneumoniae were both 0.61, P < 0.05).

The heterogeneity between trials for the various network models showed low to moderate heterogeneity for most of the analyses (table E in appendix 1).

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The models showed low-resistivity anomalies around the earthquake area that imply crustal fluid flows.

Table 7 summarizes all of the analysis results related to the regression fits, with RMSE and adjusted R 2. Table 7 and Fig. 4 show that for injuries (I) and fatalities (F), the models show low fits compared to those for home loss (H).

Remarkably, population education level did not remain in the models, showing low power in explaining variation.

We thus proposed that these models showing low endogenous ROS, would also exhibit elevated levels of the inhibitory (SH 2Trx-ASK1 complex.

An artificial neural network was applied for the multivariate optimization of the adsorption process from the experimental data of the univariate optimization study and the optimized model showed low values of error functions and high R2 values of more than 0.99 for As III T and As V T.

As could be expected, the initial model showed low predictive ability, the squared correlation coefficient between acd_logd values of test set compounds and the predicted values being (hbox {Q}^{2}=0.501).

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