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The Error Correction Model displays better fitting accuracy and forecast precision than the MLR model.
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Compared to the standard positive feedback model (SF), however, the FF model displayed better tracking, but reduced polarization (Fig. 4B).
The adjusted SOFA model displayed better discrimination (AUC 0.79, 95% CI = 0.76 to 0.82).
When this threshold setting was applied, the models displayed better prediction accuracy for all three traits studied compared with the individual SNP approach.
It is obviously observed in Fig. 11 and Additional file 1: Figure S4 that the Langmuir isotherm model displays a better fit to the experimental data for both BiOCl and Fe/BiOCl than the Freundlich isotherm model, which indicates the monolayer coverage of the surface of BiOCl and Fe/BiOCl by RhB molecules.
Both models yielded adequate predictions, but the hybrid PLS-RBF model displayed marginally better prediction accuracies.
However, predictions made with a numerical model display a better quantitative agreement with the measurement results.
For the kinetic study, Avrami kinetic model displayed a better fit with kinetic data at all temperatures studied.
We chose ϵ = 40 for the ABC inference of BDP model parameterisation to ensure these models all displayed better fits to data than the alternative models.
Our model displayed a somewhat better fit than that of Pladevall et al., and the factor loading of all variables was >0.3, indicating acceptable construct validity (Fig. 1 C).
Both models displayed a better predictive power using VIP selected spots compared with randomly selected ones (Fig. 1).
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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