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Performance of the methods was assessed by quantifying the sensitivity, specificity, and z-score.
The validity of the proposed methods was assessed using the standard addition technique.
Outcomes for the three measures were compared between groups and correlation between scoring methods was assessed.
The impact of changes in methodology based on the intended use of the methods was assessed.
The subjective quality of two BWE methods was assessed by using the pair-wise subjective preference tests.
The accuracy of the proposed methods was assessed by analyzing certified reference (CRM); Virginia tobacco leaves (CTA-VTL-2).
The reliability of the scoring methods was assessed by the correlation between the TRIP total score and the penalty total score at both baseline and post-training evaluation.
The performance of all the seven machine learning methods was assessed and compared by the predictive power and application domains of the models to the external test set.
Moreover, soil classes were predicted in the interest region using direct soil observations; finally, the accuracy of the soil maps obtained from both methods was assessed.
The quality of the different methods was assessed using a two step validation approach: (i) Cross-validation was used to evaluate the capability to reproduce measured data.
The performance of all methods was assessed in several ways: At first, differently corrected RCM data was compared to observed climate data.
More suggestions(16)
means was assessed
mode was assessed
methods currently assessed
methods was discussed
methods was found
methods was tested
methods was calculated
methods were assessed
methods was welcomed
methods was compiled
methods was used
methods was compounded
methods was observed
methods was made
methods was checked
methods was established
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CEO of Professional Science Editing for Scientists @ prosciediting.com