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The obtained classifications were then compared to the conventional physicochemical water quality index currently in use in Morocco.
Visual estimates or classifications were then made of dominant bank material, percent stable bank, percent undercut bank, dominant original bed material and dominant depositional bed material, original habitat embeddedness , and depositional habitat embeddedness.
These binary classifications were then used in logistic regression.
The single and multiple pathology classifications were then used to predict longitudinal change in each of the cognitive outcomes and in two derived composites.
Job classifications were then dichotomized between non-managerial (groups 1, 3, 4, 5 and 7) and managerial occupations (groups 2 and 6) [referent] according to Census group descriptions.
The motifs most relevant to the heart and cell type-specific motif+ChIP classifications were then grouped according to the protein families of their respective TFs (Fig. 4).
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The tool of visualizing and analyzing the classifications is then proposed.
The K-means classifications are then used to assign marker scores.
These four classifications are then contextualised further into sparse and less-sparse based upon the housing density at a wider geographical scale.
The predicted classifications are then compared to the actual experimental treatment of each sample to determine the overall accuracy of the RF model.
Age and protocol risk classification were then used to create a registry-based risk algorithm.
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