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The game's use of new model sets were also showcased.
Five model sets were considered for the model selection (Table S2).
For the low-density dataset, the values of ĉ calculated for each model set were considered too high (> 3) to be incorporated directly into the correction of AIC, so the model sets were compared with values of ĉ ranging from 1 to 10 to look for major discrepancies in first-year survival estimates and model weighting [see Additional file 1, tables 1 to 3].
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These model sets are trained by HTS.
Thus, the difference in the state transition distributions of two model sets is taken into account.
These gene model sets are very likely to include recent pseudogenes; we examined overlap with the other annotated gene sets for additional model support.
The final model sets was then rerun using restricted maximum likelihood estimation and residuals were assessed visually for heteroscedasticity and normality.
Both figures of merit (sensitivity and specificity) for the model set were equal to 95.24 %, whereas they were at a level of 90.00 % for the test set samples.
Our model setting is for an asymmetric shear deformation.
The model set was tuned to the data of each patient at baseline conditions.
The model set was evaluated in 12 and 5 patients on volume and pressure support respectively.
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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