Sentence examples for model predicts as from inspiring English sources

The simplified model predicts, as accurately as the full mechanistic model, the biofilm thickness and organic substrate flux.

Our model predicts, as our experiments show that the presence of an extra fold in the four-mirror ring cavity permits a more stable bi-directional mode locked operation.

He introduces an "uncertainty shock" into the model by supposing that the variability of firms' revenues suddenly rises: using data from shocks over the past 45 years, he supposes that the standard deviation, a common measure of variability, doubles before returning to its old level within a few months.Wait a momentThe model predicts, as does Mr Bernanke's, that firms wait and see what happens.

This would deliver roughly the behavior that the model predicts as optimal.

The Bayesian model predicts (as many other models would) that when one keeps the visual noise level constant and increases auditory SNR, the frequency of reports of the auditory word will increase and the frequency of reports of other words will decrease.

In this sense, our model predicts as a result what is assumed by numerous models, namely that the majority of a robust population will undergo at most one rearrangement (or even one mutation if the rates are similar) per generation.

In the range of 20 000 to 50 000 main SNPs used to select QTL-haploblocks, the P-values were high (greater than 0.6), suggesting that those models predict as well as or equally well as the model using all haploblocks.

What does this model predict as a social science experiment.

Analysis of variance (ANOVA) was used to confirm the significant and adequacy of the quadratic model predicted, as shown in Table 6.

This Bayesian analysis was supported by similar analyses based on Maximum Likelihood, Neighbour-Joining and Parsimony methods, as implemented in the MEGA6 package (Additional file 7: Figure S5) using the JTT + G_I model predicted as the most likely substitution model (Additional file 8: Table S3).

ROC curves were constructed by plotting true positives (patients who died and whom the model predicted as dying [i.e. sensitivity]) versus the false positive fraction (fraction of the patients who lived and were incorrectly classified as dying [i.e. (1 – specificity)]).