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Note the difference of more than 3 orders of magnitude between the error measures.
On the other hand, we found that data transformation after preprocessing and normalization can mask mutual relations between the error measures including also the fraction of differentially expressed genes.
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The correlation coefficient between the errors measures the extent of correlation between inorganic fertilizer and improved maize varieties adoption decisions.
In the supplementary text (Additional file 1) we studied the relation between different error measures and the fraction of differentially expressed genes more in detail.
In Equation (10) (or 11), δ c (P 1 ′ ) (or δ c (P 2 ′ ) ) is the difference between the errors corrected measures of E i + 1 (P 1 ′ ) (or E i + 1 (P 2 ′ ) ) and E i (P ″ ) ; and δ d (P 1 ′ ) (or δ d (P 2 ′ ) ) is the difference between the fragments cut measures of E i + 1 (P 1 ′ ) (or E i + 1 (P 2 ′ ) ) and E i (P ″ ).
A one-way between groups ANOVA of the remaining error measures showed a statistically significant main effect of group (Table 3).
To determine this we examine the relationship between the error and the true measure of BMI.
A significant slope between the error rate and the AD measure can only be found for the confidence measures, while for the novelty measures the slope is small or insignificant.
A number of previous validation studies, including the present work, have confirmed good agreement between the level of experimental error measured at low CTC and the theoretical level of error dictated by Poisson statistics [ 16, 27].
We analyze the proposed architecture in terms of peak signal-to-noise ratio as well as mean squared error measured between the original and steganographic files averaged over all video frames.
In this case, the difference between the measured errors in the SAME and DIFF conditions became dramatic (Fig. 11).
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