Sentence examples for models have significantly from inspiring English sources

A Yale University study says global climate models have significantly underestimated how much the Earth's surface temperature will rise if greenhouse gas emissions continue to increase as expected.

Overall, bioengineered liver models have significantly advanced our understanding of liver function and injury, which will prove useful for drug development and ultimately cell-based therapies.

German Automobile Industry Association President Bernhard Mattes noted in a statement last month that the latest models have significantly lower nitrogen dioxide emissions, and that their comparatively low carbon dioxide emission levels will be critical to meeting Germany's aggressive climate-change targets.

Identification of the intestinal stem cell marker Lgr5, isolation and in vitro culture of Lgr5+ intestinal stem cells and the use of transgenic mouse models have significantly facilitated the studies of intestinal stem cell homeostasis and differentiation, therefore greatly expanding our knowledge of the regulatory mechanisms underlying the intestinal stem cell fate determination.

Bayesian hierarchical models have significantly helped to cope with sparseness in disease-mapping studies (Best et al. 2005).

Recent studies in relevant preclinical models have significantly advanced our understanding of the effects of hypercapnic acidosis in both pulmonary and systemic sepsis-induced ALI/ARDS.

As fashion weeks mushroom across Iran, the need for more models has significantly increased.

IPCC vice chair Francis Zwiers, director of the Pacific Climate Impacts Consortium at the University of Victoria in Canada, co-wrote a paper published in this month's Nature Climate Change that said climate models had "significantly" overestimated global warming over the last 20 years -- and especially the last 15 years, which coincides with the onset of the hiatus.

With the exception of the Glinsky et al. 2004 Signature 1, all of the gene/probe models had significantly lower AUCs in the validation set compared to the training set. Figure 2F compares the training and validation set AUCs of each of the gene/probe models including clinical model C.

It points out that the two locally customized score models had significantly lower discrimination capacities than the Bayes linear classifier.

Both of these decision models had significantly poorer predictive performance than the original decision model; in fact, their performance was sometimes close to chance.