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To validate the numerical models, a test stand is build.
For both models, a test for the full model with all three predictors against a constant only model was significantly different (P < 0.01).
We found that models A (test 1) and B only fitted the data significantly better than simpler models when human RPS4Y2 lineage was used as the foreground branch (Table 2 for model A); this indicates that positive selection acted on some sites of the human RPS4Y2 gene.
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While potentially a tough sell for users like myself who've had a sub-par experience in the past with earlier and cheaper models, a test-drive easily argued the case for entry and intermediate level users.
To identify the model, a test rig was designed and manufactured.
To verify the validity of the proposed model, a test platform was constructed as shown in Fig. 8.
Finally, using the QLT model, a test for differences in latent quantitative literacy scores by cohort was conducted.
For evaluating the performance of the model, a test set having 12,854 records was applied to the proposed system.
The CODEML program in the PAML package [78] was used to run the branch-site-specific model A, test 2 [77], which infers selection on codons along specified branches.
Two different models (model A test 1 and model A test) were implemented (Zhang et al. 2005).
Three models are presented: a pragmatic model, a test model, and an ideal model.
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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