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Since the building of the model is dramatically complicated and cumbersome, in this section modeling process is described briefly and shown in Fig. 11.
The new model is dramatically smaller than the old one — 1/4 of the size actually.
The experimental results showed that the predictive power of a nominal prediction model is dramatically reduced when using a low complexity algorithm instead of a high complexity algorithm.
Moreover, the fit of the five-factor model is dramatically and significantly (Δχ = 3059.44, p < 0.001) better than that of the one-factor model.
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From the results, we can see that the predictive power of the four-feature discomfort prediction models is dramatically reduced by the use of a low complexity stereo algorithm instead of a high performing, high complexity algorithm.
The number of necessary ODEs in layer-based models is dramatically reduced.
These models generated the same stationary family size distribution, but the stochastic properties of the higher order models were dramatically different from those of the linear BDIM.
While the number of fully sequenced genomes continues to grow at an exponential rate, the number of published reconstructions of metabolic models [ 2] is dramatically lagging behind the sequencing effort.
It is found that the thickness effect curve resulting from this equation of state and reaction model using published values is dramatically more steep than observed in recent experiments.
The averaged Akaike information criterion (AIC) for model 4 was dramatically lower than that for model 3, implying that model 4 was not better than model 3.
However, the relationship between model parameters and dominance values is dramatically weaker than for additive values.
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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