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Errors are small when we compare numerical solutions with analytical solutions.
This means that the image errors are small compared to the 6-12 bits dynamic range of the red, green, and blue channel image information.
The step size for time integration is chosen so that temporal discretization errors are small relative to spatial errors.
Experimental results show that the algorithm perfectly follows contours as the cycles approach infinity regardless of whether tracking errors are small or large.
Estimation errors are small, with root mean square errors of 0.007 and 0.008 cm3 cm−3 respectively in 2008 and 2009.
The thinning process generates subsets of "most significant" points, such that the piecewise linear interpolants over the Delaunay triangulations of these subsets approximate progressively the function values sampled at the original scattered points, and such that the approximation errors are small relative to the number of points in the subsets.
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The results show that: all precision errors are smaller than 15% and average precision errors are smaller than 10%.
Most of the crossword errors are smaller and more subtle than the above.
Additionally, the errors are smaller than the ensemble forecast spreads.
The errors are smaller than the forecast spreads.
As can be seen, for both techniques the mean errors are smaller for higher partials.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com