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Consequently, the standard error, (text {se}(hat {vartheta })), of (hat {vartheta }) can be estimated by the sample standard deviation of the G replications, i.e., widehat{text{se}}(hat{vartheta}) = left{ frac{1}{G-1}sum_{g=1}^{G} left[hat{vartheta}_{g}^ - (hat{vartheta}_{1}^ + cdots + hat{vartheta}_{G}^)/Gright]^{2} right}^{1/2}.
The errors (text {LE}_{mathrm {o}}(hat {phi }_{mathrm {d}})) and (text {LE}_{mathrm {u}}(hat {phi }_{mathrm {d}})) are plotted on top of each other, such that the total bar height shows the total error (text {LE}(hat {phi }_{mathrm {d}})).
It functions as the null point of membrane voltage where there is no error (Text S1).
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At the lowest thresholds, as link-prediction error increases, (text {OAS}_{text {tg}}) does decline to match the random-seeding baseline.
Due to a translation error, the text of this article originally appeared in Dutch.
Descriptive statistics will be reported as mean and standard error in text and figures.
The differences between the recording and the current predicted by the model were quantitated as the relative least squares error (see Text S1).
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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