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In order to setup the model, the digital elevation model, land use/land cover and soil map were projected into common projection system.
Thus, 65.89% of the variation in the measured ITS values are explained by the model, whereas in experiment 2.2 with the CUP loading geometry, the RM has an R 2 = 73.60% and in experiment 2.3 with the FUP loading setup, the model has an R 2 = 53.21%.
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In the standard DNA setup, the GTR model was used with all model parameters estimated from the data, with among site rate variation modelled with gamma distributed rates across sites with four discrete rate categories.
In fact, changing the setup of the model does not change the implication greatly as I include adult family members' income.
As a consequence, the very setup of the model prevents one from obtaining analytical results, so that numerical methods must be used to solve for the equilibrium and the response of the economy to government policy changes.
This setup has the model calculate an average for each day based on the polls available prior to that date and 100,000 simulations of the polling.
Our setup has the model calculate an average for each day based on the polls available prior to that date and 100,000 simulations of the polls in what are called Markov chains.
We will first describe the experimental setup and the model itself, followed by the actual predictions.
There are strengths and weaknesses in the setup of the model that require consideration before interpreting its predictions.
A multi-objective calibration approach was utilised to setup the HYPE model at the Weida catchment.
In this setup, the predictive model provides perfect predictions for patient's future states because predictions and future observations are the same at every time point.
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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