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We present the pipeline of our complete system, including feature selection and model evaluation steps.
These model evaluation steps are often considered sufficient qualification for the typical uses of a PKPD model: selection of doses in a trial, description and interpretation of longitudinal study data, etc.
Objectives (i) and (ii) are semi-quantitative, therefore, previously listed model evaluation steps are considered sufficient to qualify the model as fit for these purposes even if the predictions and observations do not entirely agree.
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Multi-label dataset, model (MMM) and evaluation procedures are shown in blue and single-label dataset, model (SMM) and evaluation steps are shown in green.
Model evaluation is a crucial step in pharmacometrics, and it is still lacking standardization and/or consensus.
The fourth step was model evaluation and debugging.
The next step of model evaluation examined its ability to reproduce experimentally measured data which had not been used for parameter identification.
The coherency of cropping system assessments has been judged by experts and found consistent, constituting the first step of the model evaluation.
The last step of the model evaluation stage deals with the generalizability/external validity of the animal model.
This initial step is followed by model evaluation, where the variability predicted by the model can be a useful test parameter.
Our approach iterated through four steps: data collection and preprocessing, hypothesis generation, model evaluation and experimental feedback (Figure 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