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The validation data sets confirm the training results: combining the three individual models significantly improved classification in 4 out of 6 data sets, while no significant improvement was obtained in 2 out of 6 data sets.
The results of this study show that use of the regularized constitutive models significantly improved the accuracy of response predictions.
Thus the PM and MSW models significantly improved the accuracy against the original SW model and can be used to estimate ET and Es under the film-mulching condition.
Implementing long-term MAP as a second predictor variable into the VI LAI models significantly improved LAI predictions by up to 10% using the normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI 2) and 2-band enhanced vegetation index (EVI 2).
Further, the combination of the different WVC models significantly improved the F-measure.
Our models significantly improved the fitting performance of the postseismic time series and the prediction performance of the evolution of postseismic deformation.
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We concluded that two-dimensional local saturation information in larger scale three dimensional reservoir models significantly improves the interpretation of the recovery mechanisms in heterogeneous porous media.
The main conclusion is that the use of more refined Bayesian network models significantly improves the results.
In particular, the rigid body models significantly improve the fit in the intermediate q region of 0.08 0.16 Å–1.
We show that, both in the atpB and rbcL alignments of a collection of land plants, these context-dependent codon partition models significantly improve model fit over existing codon partition models.
Modifications to the model significantly improved its predictive capacity for equibiaxial loading data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com