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These classifiers were applied on the test samples we describe below.
B. The model ensemble is then applied on the test set.
Next, the optimal parameter set is applied on the test data to hypothesize the detections of the test term list.
Heat treatment was applied on the test samples in an oven for three final temperatures (180, 200 and 220 °C).
Then, the proposed method is applied on the test image, with α = 60 (see in Eq. (13)) for the search of peaks and troughs in S4.
Table 3 lists the PSNR and UIQI values that are reported by the different algorithms, applied on the test images with different noise levels.
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The Benjamini-Hochberg method was applied on the tests shown in Tables 2, 4, and 5.
This model performed very well over the range of data used for training, with r-value of 0.95, mean square error (MSE) of 0.0019 and mean absolute error (MAE) of 0.024 mg/l when applied on the testing data set.
The cut-off of the interestingness measures were applied on the testing set.
These thresholds were applied on the testing set and allowed the calculation of several indices (Table 3).
Using the training set, the parameters in Eq. 2 were estimated, which were directly applied on the testing set to compute the EC-based LogFC scores using Eq. 5. We found that the parameters for the three types of seed measure groups (high, low and mix) are significantly different, reflecting different integration strategies.
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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