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Mean errors in automatic volume estimation ranged from 0.07 to 0.6%, depending upon capillary diameter.
The results demonstrate clearly visible segmentations of bone surfaces with a localization accuracy of <0.6 mm and mean errors in estimating fracture displacements below 0.6 mm.
The mean errors in glaciers, deserts and wetlands were −1.05 m, −2.03 m and −2.43 m, and 1.05 m in built-up areas.
With this phantom, the mean errors in the measured coordinates of the control points were on the order of 0.1 mm or less, which were less than one tenth of the voxel's dimensions of the phantom image.
The good agreement between the experimental and theoretical data (mean errors in the range 2 7.84%) shows that the proposed unstructured model is adequate to describe the growth of the bacterial strain.
Fitting errors for each point were calculated as mean errors in α using an X-fit routine [32].
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The results for both algorithms are compared and the relative mean error in axial direction is 0.30% and 0.48%.
After updating, the mean error in natural frequencies is decreased from 3.23% to 2.34%, and the average MAC number is increased from 0.87 to 0.94.
Table 6 Mean error in dB for different RMS ratios.
Table 9 Mean error in dB for vocal signals.
Fig. 8 Mean error in abstract magnitude number line estimations (error bars = standard deviation).
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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