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In the both cases, the linear models also performed well and were pretty close to the best model.
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.
Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets.
The neural networks models also performed very well.
Most other models also performed at or below chance (see Figure 5).
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Random parameter models also perform well in describing the parameter heterogeneity (Mannering et al. 2016).
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.
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.
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