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This comparison shows the good agreements between two predictions, which support the above arguments for the error and the successful application of the time-domain CAA methods.
As one cycle of the burst is equal to 0.048348[deg] and 2100[pixel], the conversion equation is begin{array}rcl@ B_{text{pix}}left(Dright) = frac{2100}{0.048348} B_{text{deg}}left(Dright) end{array} (4). Figure 1 shows the comparison between two predictions and the actual offsets used in Lindsey et al. (2015).
An example is shown in Figure 3 revealing the relation between two predictions at different time points.
Similar(57)
The estimates for Dxy, R, and AUC are useful for comparison between two prediction models, but the actual values, such as Dxy of −0.333, R of 0.311, and AUC of 0.84, are uninformative to infer the significance of the prediction.
The congruency (P-value = 1.8 × 10−34) between the two predictions was mainly driven by the large number of SNPs predicted to be benign by PolyPhen and tolerant by SIFT.
There was highly significant non-independence (P-value = 1.8 × 10−34) between the two predictions, driven primarily by the large proportion (66%) of SNPs predicted to be benign by PolyPhen and tolerant by SIFT.
Through these comparisons, it is shown that there are good agreements between the two predictions.
The plane strain model is verified by comparing with the thin-plate model, and good agreements have been achieved between the two predictions.
It is concluded that the quantitative difference between the two predictions stems from different assumptions on how the properties vary across the phase interface yielding a lower dissipation of Gibbs energy by diffusion in the phase-field simulations.
Predicted demand is matched with the environmental and historical data if the difference between these two predictions is zero.
Although it seems that Lattice Boltzmann model underestimates the maximum experienced velocity at the half-way width of the channel with respect to the analytical approach, the difference between these two predictions fades away in greater time steps.
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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