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Depending on which of the two geological models fits better to the actual Indian motion path, one should expect different biogeographic and phylogenetic patterns as both models allow different spatial and temporal dispersal pathways.
Therefore, instead of deciding which of these models fits better to the data, researchers are now trying to find out which conditions promote elemental or configural learning and to develop formal models that can incorporate both processing strategies (Schmajuk & DiCarlo, 1992; Wagner, 2003).
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These models fit better; however, sometimes their kinetic parameters are inconsistent, resulting in an incorrect interpretation of the reaction mechanism.
In the alkaline catalysis, kinetic models fitted better a pseudo 2nd-order reaction, while for the acid catalysis pseudo 1st-order reaction provided a best fit.
As previously shown [12], [13], both LCAP and ECM models fit better than a single rate model, at least for the Pandit alignment (Figure 1).
As will be seen, neither of these models fitted better than the PO model.
A likelihood ratio test was used to determine if the variance-adapted models fitted better than the simple model.
GMM models fit better to the data than LCGM models as shown by lower AIC and BIC levels.
Formal comparison between spline and linear models based on the AIC indicated that the linear models fit better.
The models fit better than when adjusting for seasonality with a high degrees of freedom spline of time.
Figure 3, panels A, B, illustrates that models fit better to the deaths from infectious ID but still show some association with the deaths from noninfectious ID.
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