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The external prediction accuracy for binary models was as high as 91 96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76 0.79.
For some selected trees, the correlation coefficient obtained between manually measured volumes and those obtained from the 3D LIDAR models was as high as 0.976.
When there was no dominance, response to selection for the dominance model was as high as for the additive model, indicating robustness of the dominance model.
The overall risk prediction accuracy rate of our model was as high as 60%.
In this work, etch rates of hexatriacontane, used as a model polyolefins, as high as ∼10 mg s − 1 m − 2 in late O2 post-discharge are obtained at 333 K where no significant UV nor VUV irradiation occurs.
In most of the comparisons, it achieves predictive power and model separation as high as the optimal hold-out partitioning scheme.
The factor structure for the staff ratings is presented in table 2. Congruence with neither the DIP-Q nor the consensus model was as high as that found between the two versions of the DIP-Q, or between DIP-Q and the consensus model.
Although the predictive accuracy of our clinical variable model was almost as high as our model using gene expression data, this is likely explained by over representation of "extreme phenotypes".
The range estimation errors associated with the ME models are less than 2% for all the benchmark mission profiles considered, whereas some traditional models yield errors as high as 20 80%.
The comparison of the most realistic case to the one comparable to historic calculations show that the difference in temperature predicted with these two models can be as high as 300 °C.
Single particle electron microscopy (EM) is one such technique that produces models at resolutions as high as 4 Å [1], [2].
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