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However, various other models also perform well and no single model emerges as clear winner.

For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model.

Random parameter models also perform well in describing the parameter heterogeneity (Mannering et al. 2016).

The other models also perform best except the MT1 with single input H value. Figure 6 shows a graphical comparison between measured and estimated discharges.

The simplified equivalent WT models also perform well at different operating points, showing the frequency change tendencies and damping ratio of EOM.

Second, our models also perform well because, unlike previous studies, we do not impose any biological mechanisms on our models (e.g., a 'thermodynamic score' for protein DNA interactions).

The neural network models also performed better than the multiple regression ones, especially in reducing the scatter of predictions.

The O3 models also performed well in the hindcast mode, with R2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities.

Each of these models also performed well in initial quality surveys and new-car reviews, including our own experiences with road testing the vehicles over the years.

In the both cases, the linear models also performed well and were pretty close to the best model.

Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets.