"accuracy distribution" is a correct and usable phrase in written English.
It refers to the spread or distribution of accuracy within a particular context, such as a test or measurement. Example: The professor analyzed the students' test results and found that there was a wide accuracy distribution, with some students scoring very high and others scoring very low.
The fracture fixation and bone aligning task of these implants call for specific accuracy distribution, while the distinct geometry and post forming heat treatment influence production accuracy.
Fig. 3 The accuracy distribution in terms of RMSD (a) and the energy difference ((Delta E=E_{mathrm{after}}-E_{mathrm{before}})) distribution (b) of the peptide models before and after minimization with AMBER for the peptides with 10 amino acids.
Yu et al. [14] conducted the accuracy distribution for a large CNC gantry guideway grinder based on the reliability theory, in which the reliability model is approximately established by response surface method with error samples from 25 sets of position of the grinder working stroke.
Figure 4 present the accuracy distribution for different TFBS identification methods on the 13 datasets.
This corresponds to the percentile where our target score falls onto within the accuracy distribution for N samples.
For each of the control genomes, 1 million Illumina reads with the same length and base-calling accuracy distribution were simulated.
Accuracy distributions between models were compared by a paired t-test.
From the boxplot, we can see that the performances between VFDT and iOVFDT are so close; their accuracy distributions are very similar, and there is no outlier in their distributions.
We observed a robust difference in mass accuracy distributions (defined as the difference between observed precursor mass and expected peptide mass, in units of parts per million (ppm)), with the "true positive" PSMs centered around 0.127 ppm with a standard deviation of 0.637 ppm (following mass recalibration), while "false positive" PSMs were more evenly distributed throughout the mass range.
Although theoretical chance level lies at 50% (binary discrimination), empirical discrimination values might exceed this threshold (false positives) due to the large number of independent tests, the low number of observations, and possible non-gaussanity of accuracy distributions (Golland and Fischl, 2003; Stelzer et al., 2013).
Even though the comparison of 99-point and 20-point scales was not the main purpose of their study, Mickes et al. (2007) showed that 20- and 99-point scales yielded similar confidence-accuracy distributions.
