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Both models were successful at reproducing either the OC or the SCD, but did not incorporate the mutual interdependence between the factors that shape the two domains, and were less parsimonious with system components than the model we describe here.
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Although both PLSR and RF models were successful in modelling C fractions, RF models appear to target the physical properties linked to the property being analysed, and may therefore be the better modelling method to use when generalising to new areas.
Both the FB and CPFD models were successful in resolving key issues in 3-D, whereas 2-D models tended to seriously underpredict particle volume fraction, especially near the wall.
Filling-in models were successful in predicting psychophysical data for brightness perception.
All existing models were successful in illustrating different aspects of CPG function while (necessarily) neglecting others.
Therefore, the DFT models were successful in predicting the NDMA yields of these two precursors.
Graphical analyses confirmed that the variance function models were successful in accommodating the error heteroscedasticity.
These models were successful in causing aortic valve disease, but none of them was able to explore the reversibility of the process.
All models are successful in dealing with the problem of autocorrelation.
On the other hand, it is by no means clear that these connectionist models are successful and generalizable (scalable).
Crop models are successful in predicting the impact of environmental changes on crop productivity.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com