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The surface pressure distributions on the models were measured, and the drag and lift coefficients were determined by integration of the surface pressure distributions.
The performances of the models were measured using area under the receiver operating characteristic curve (AUC) [ 40], which is frequently used to evaluate prediction models in the biomedical informatics field [ 41].
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The overall performance of the models was measured by the combined matching frequencies.
Performance of the models was measured using precision and recall after application of the various class-specific binary thresholds (previously calculated) for each target.
Finally, the performance of the models was measured and compared.Area under the ROC curve (AUC) and agreement (kappa) between the content topics predicted by the models and those chosen by clinicians in each infobutton session.The performance of the models ranged from 0.49 to 0.56 (kappa).
The calibration of the models was measured using calibration curves [ 39].
The predictive ability of the models was measured with the Harrell′s concordant statistics (c-index) with 95% CI.
Performance of the models was measured using an R2 value, Clarke error grids (CEG) and rate-error grid analysis (R-EGA).
All parameters of the model were measured independently.
Drift demands on the model were measured to be higher when the structure supported a squat block as compared to a slender block with the same mass.
The accelerations and the deformation phases of the model were measured by a data acquisition system and an image processing system.
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