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The model validation revealed comparable results as the model calibration for both crops (Tables 2, 3).
However, model validation revealed a poor model fit for SSFs of wolves moving to kill sites (Table 4), and we are therefore unable to conclude whether and how wolves chose kill sites in relation to human infrastructure.
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Model validation reveals that simulations and measurements of the spatial distribution and energy status of parasitoids are generally in good agreement.
A verification experiment was carried out to examine model validation and revealed more than 99% validity.
The Results from the Model Validation are revealed as follows.
Model validation results revealed an acceptable modelling of facade surface temperature, air temperature, relative humidity and wall-emitted long-wave fluxes.
However, the model validation with revealed a poor fit for this model, with too many serotypes present and too little clustering.
Regression models, together with experimental validation, revealed that latently infected cells were sensitized to pro-death signaling via the p38 and JNK MAPK pathways and that the expression of viral proteins increased this effect.
Model cross-validation revealed an AUC of 72.3%.
Model validation results reveal that inaccurate geological models yield poor drawdown predictions, but using more calibration data improves its predictive capability.
But while model validation may reveal useful information, this paper argues that it is not possible to demonstrate the predictive reliability of any model of a complex natural system in advance of its actual use.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com