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Table 8 shows the variance of the estimated fixed effect (hat{beta }_3) across scaling models calculated for each country separately.
When we took a closer look at the results emerging from the use of the variance of the different estimated fixed effects across scaling models given the country, that is (s^2_{hat{beta }_{jgz.g}}), we found virtually no variation across the models for the estimated fixed effects (hat{beta }_0), (hat{beta }_1), and (hat{beta }_2).
This finding corresponds with the invariance of the observed distribution of the fixed effects (hat{beta }_0), (hat{beta }_1) and (hat{beta }_2) across scaling models: when three out of five fixed effects are virtually unaffected by the scaling procedure, no overall effects (as measured by the F-type statistic) can be expected.
Without conducting analyses across scaling methods, it would be unclear whether the estimation process, type of outcome, or other factors biased the results.
In addition to analyzing the global tests of significant difference between the fixed effects, we analyzed the variances of the respective fixed effects across scaling models (given a country) and the variance of the fixed effects across countries (given a fixed effect).
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It's true that you can't do things consistently, reliably and across scales without additional payment.
Extrapolating across scales is a critical problem in ecology.
To allow comparability across scales, a dataset with equivalent predictors was obtained for the local scale.
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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