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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.

This includes better design of animal models as well as higher general study quality, such as blinding, randomization or avoiding underpowered studies.

Single particle electron microscopy (EM) is one such technique that produces models at resolutions as high as 4 Å [1], [2].

Even when we focused on the very short length group ([60 100 nt)), our model provided sensitivity as high as 83.44% and specificity as high as 92.8%.

The opposite was seen in LA, where the median risks from the U.S. EPA model were twice as high as the personal risk estimates.

Then, the electrical behavior should comply with the BC model as high porosities (>50%) should be reached.