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At last, the different model variants are compared with modeling approaches described in the literature.
AD helps to simplify implementation of the computational code and is much more flexible when model variants are considered.
Obviously, few – if any – of these model variants are available on the market throughout the period.
A few words on the selection of model variants are in order.
Four model variants are compared for each resolution: the content-blind and -aware quality-based models, and the content-blind and -aware impairment-factor-based models.
Some vehicle model variants are very similar – indeed, in some cases deciding whether two cars represent two different model variants or two versions of the same model variant may seem like a matter of fine judgment.
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The following model variants were considered.
Several model variants were produced using different estimates of the quality of the water leaving the root zone and contrasting methods of weighting the input data.
These model variants were extensively tested under unsteady and steady flow conditions using the numerical experiments, and USGS laboratory and real-world river application datasets considering the popular MIKE11-AD model as the benchmark.
Other model variants were tested, representing alternative hypotheses about the action of the auxin gradient.
Overall, while the variations in mean deviance (Figure 2b) seen between model variants were much smaller than for the models conditioned on the first infection (Figure 2a), the interaction model allowing for time varying cattle infectivity gave the most adequate fit (measured by both mean deviance and DIC, see Table 2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com