The overall sensitivity and specificity over the predictions is nearly the same between the two predictors.
To the extent that nutrients and SST are correlated, the statistical model is unable to distinguish the effects of one predictor from the other; the chlorophyll response is divided between the two predictors.
The interaction value can also be thought of as the relative contribution of the interaction between the two predictors towards the overall predictive performance of the individual model (the cv deviance value).
During the testing process, only the sequences of proteins in Online Supporting Information S1 but not their accession numbers were used as inputs in order to make the comparison between the two predictors under exactly the same condition.
During the testing process, only the sequences of proteins in Table S1 but not their accession numbers were used as inputs in order to make the comparison between the two predictors under exactly the same condition.
For example, scleractinian coral white syndrome outbreaks along the Great Barrier Reef (GBR) require a threshold coral cover of greater than 50% in conjunction with thermal stress events, and the interaction between the two predictors explains a significant amount of the increase in the frequency of outbreaks [31].
The null hypothesis of the Pearson's linear correlation was that there is no correlation between the two predictors.
As a consequence, Genemark predictions were always prioritized over Fgenesh prediction in case of inconsistent annotation between the two predictors.
For major morbidity, the AUC was comparable between the two predictors, being 0.73 (95% confidence intervals, 0.61 to 0.86) for nadir ScVO2 and 0.73 (95% confidence interval, 0.61 to 0.84) for peak lactate.
All correlations between the four predictor variables openness, ideational fluency and originality, and intelligence were statistically significant.
