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A new detection statistic is derived for validation of the plant model regardless of how the disturbance model changes.
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On the basis of the input and output parameters, a methodology is derived for the validation of the model by using full scale running tests.
A Galerkin method solution is also derived for comparison and for validation of the formulations presented.
Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except r2m in DTF), thus put a high confidence in this analysis.
Because methods 1 to 4 use cross-validation, predictive accuracy was calculated for each of 15 three-fold cross-validation replicates derived for each simulated dataset.
CDKN1C/GAPDH ratios were derived for each run.
This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation.
A 95% exact binomial confidence interval was calculated for each parameter derived from classical validation and CRS analyses.
An estimated false-discovery rate of 1.3% for SNVs was derived by the validation of a further 155/157 homozygous SNVs randomly selected from the rest of the genome.
The accuracies of the mBVs for each breed were derived from the validation animals using two different methods.
Identified physiological model parameters and model outputs were compared with experimentally derived indices and measurements for validation.
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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