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{A}_{agg}left(2@87%right)=0.7982+{0.1536}^{ast }{A}_{var} (3) Fig. 3 Accuracy Change Impact for One Model with Two Models at 87% Fig. 4 αAMP for One Changing Model with Two Models at 87%. Figure 4 studies the amplification impact of a varying model when aggregated with two models of average accuracy of 87%.
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We tested 3 principal models with varying model parameters.
The final step involves finding the full varying coefficient model when more than one variable with varying coefficients are required in the model.
For example, there were varying models of community mobilization interventions.
It may be useful to consider the conversion of the layered structure model to a smooth depth-varying structure model when constructing a 3D velocity model of sedimentary structure for long-period ground motion simulations.
Recall that a ROC curve shows all possible couples (1 - specificity; sensitivity) achievable by a prediction model when varying the threshold probability.
We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration.
We may have to adjust the R-Q model when the scene condition changes, such as the varying light or the effects from weather.
Varying the weight of a given reaction had no impact on the model when the variation was small (weight +/- 10%).
This example demonstrates the limitations of the queue model when accounting for the precise traffic volumes arriving in TIM phases according to time-varying traffic demand.
This can be illustrated by comparing the benefits and limitations between the proposed models when varying the weight on the cost attribute from zero to one, as shown in Tables 13 and 14.
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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