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Model comparison results show evidence about the presence of spatial effects in influencing the role of built environment characteristics to play in the commuting behavior analysis.
The model comparison results focus on aggregate projections of economic growth, energy use, carbon emissions, fuel choice, and carbon taxes required to control carbon emissions.
This introductory paper gives the motivation for the EMF 19 study on technology and climate change policy, an overview of the design of the study, some aggregate model comparison results, and a brief introduction to the rest of this volume.
Figure 7 summarizes the model comparison results for each subject, showing the difference in log-evidence for both auditory cues.
Also, consistent with the model comparison results above, parameter estimates are more precise for the dynamic model than for the static one.
Indeed, this is what our model comparison results suggest.
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Absolute bias (AB) and mean square error (MSE) were then computed for model comparison (numerical results are summarized in Table 5).
Model comparison and results are presented in Additional file 1.
In section 4, the model ensemble/measurement comparison results are presented, and in section 5, we summarize our findings and their significance.
These model comparison values result from testing on all 2431 siRNAs, across all partitions, but simply not all of the siRNAs are used to train the underlying model.
Through a retrospective model-data comparison, results show that the inclusion of the new monthly IGNS index in place of the current 12-month smoothed IG index reduce the foF2 model prediction errors by nearly a factor of two.
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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