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This comparison clearly demonstrates again that experimental data are still needed because the predictive power of nuclear model codes, though permanently improving, does still not allow reliably predicting the cross sections needed for most applications and irradiation experiments remain indispensable.
This comparison clearly indicates that experimental data are still needed because the predictive power of nuclear model codes, though permanently improving, does still not allow to reliably predict the cross-sections needed for most applications and irradiation experiments remain indispensable.
Consequently, the comparison of measured and modelled thin target cross sections clearly indicates that experimental data are still needed because the predictive power of nuclear model codes, though permanently improving, does still not allow to reliably predict the cross sections needed for most applications and irradiation experiments remain indispensable.
Therefore, the comparison of measured and modelled thin target cross sections clearly indicates that experimental data are still needed because the predictive power of nuclear model codes, though permanently improving, does still not allow to reliably predict the cross sections needed for most applications and irradiation experiments remain indispensable.
This is because the predictive power of infection betweenness centrality for NB is similar across the networks as shown in Figure 3a.
This is because the predictive power of each feature is low in OREGON than in the other networks as shown in Figure 3. Next, we compare the classifiers using all of the features to those excluding infection betweenness centrality in order to check whether infection betweenness centrality can improve the performance of the classifiers.
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1– 3 Several recent discoveries ranging from genes that drive progression of different cancers 4, 5 to microbes and microbial genes that cause a human illness 6 became possible because of the predictive power of network analysis.
Log-transformation was used because it increased the predictive power of the regression and made all regression coefficients dimensionless and comparable with each other; log10(n + 1) was used to handle zero counts as arguments.
It's important to notice that we have put in the model also two non-significant markers (TC1, Hs.296031), because their contribution to the predictive power seemed to be relevant.
This is because there is insufficient evidence of the predictive power of the UCAS statement [ 7], because assessors have concerns that unequal levels of support provided to applicants in writing the statement, and because of the difficulties of detecting plagiarism and deception.
But Fair Isaac gets a little blame, too, because its scores did not deliver the predictive power that was expected of them.
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