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For a more accurate estimate of the test error, we also calculated 95% confidence intervals for the cross-validation accuracies of the modules.
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We also calculated the error rate without wrong time errors, as this type of error is a matter of much debate.
We also calculated the error in the optical phase between two adjacent phase contours resulting in an error value of ~π/12.
We also calculated type 1 error rates of the mutual information-based statistic by simulation.
We also calculated the standard error of measurement (SEM) of the ICC [ 29].
We also calculated the misclassification error, taking an average of the class probability for each sample in all 60 cross-validation segments as compared with our previous approach in which a sample was judged as correctly classified only when a majority of members in the corresponding cross-validation segment were correctly classified.
We also calculated the mean squared error (MSE) for the results mathit{MSE} = ( {1 / N} ) biglVert {x^{ast}- x} bigrVert, where (x^{ast}) is an estimated signal of x.
We also calculated the mean relative error (MRE) using the predicted ages based on the age model and the chronological ages.
We also calculated the ray casting-based error of the KinectFusion and Go-ICP algorithms for further comparison.
To better estimate the variation in the datasets, we also calculated the root mean square error (RMSE).
For each group, we also calculated the mean number of errors made during the session (Figure 6D).
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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