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The RMSE % and RMSECV % of Model 3 (i.e., non-post-stratified model) varied from one category to another; smaller values of RMSE % and RMSECV % were reported for vegetation types as compared to land use types.
The percentage of significant variables among those selected in final model varied from 100%to27%27 %.
Random effect-sizes, calculated as standard deviation of the 'random' term in the model, varied from 73% to 189% of the feature effect-sizes.
Because the exchange rates derived from fitting the data to a two-state model varied from residue to residue, a more complex motional model appears to be required to adequately explain the dynamics.
This method of generating a model varied from Tfu_v1 and Tfu_v2 as the initial reaction list was populated from reactions associated with experimentally detected proteins, not informatics-based annotation.
The initial size of the model varied from 0.25 to 4. The result shows that when the initial cell size is smaller than 1.5, the cycle time shows a significantly negative relationship with the initial size.
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The cross-site variability (i.e., variability from site to site) makes the statistics of the bias factor of a design model vary from site to site.
In addition, for Kerman Station the attained MABE and RMSE for the ELM model vary from 1.6567 to 2.4233 °C and 0.8661 to 0.9789, respectively while for the SaE-ELM model they vary from 1.5818 to 2.3422 °C and 0.8736 to 0.9831, respectively.
The sequence distances (based on the HKY model) vary from 0.09 to 8.65% (mean 2.2%) among ingroup taxa, distances among different haplotype clades vary from 1.10% to 7.73% (Table 3).
The reasons beyond the choice of model vary from case to case: mainly economical (agriculture, medicine, conservation…), or of evolutionary importance (the first non-primate mammal, the first cetacean, etc.…).
Furthermore, by allowing the discrimination of the treatment-selection model vary from 0.5 to 0.9, we considered situations with a wide range of confounding, reflective of scenarios encountered in observational research [ 28].
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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