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The classifier works as follows: Given D, the classifier firstly creates k bootstrap samples of D, with each of the samples denoting as D i.
The model building and validation process is repeated K times with each of the samples used once as the validation set.
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For each of the top principal components we fitted a univariate linear model with each of the sample annotations as regressor.
Muscle, DNA, and white blood cells (WBC) were obtained for each animal from Texas A&M University under material transfer agreements (MTA) with each of the sample owners (American Angus Association, American Hereford Association, American Simmental Association, American International Charolais Association, and the North American Limousin Foundation).
We can also simulate the dynamic activity associated with each of these samples using the associated parameter values.
Each dog was tested with each of the 24 samples by presenting the sample to the animal for 30 45 s.
Each of the experimental samples (n = 3) was compared with each of the reference samples (n = 3), resulting in nine pairwise comparisons.
The analyses were then run in triplicate in an alternating sequence, with a blank between each of the samples.
In-field measurements of water temperature, pH, dissolved oxygen, conductivity and salinity were recorded concurrent with each of the hourly samples.
Our CRCNN framework can be decomposed into two main stages, as presented in Fig. 1 (d): After building up a baseline, the input image is compared with each of the baseline samples.
With each of the 10 samples, the immature fruits were ranked by their extent of ripening based on visual assessment of color change and skin wrinkling.
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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