Sentence examples for models are modest from inspiring English sources

Some interactions are based on a two-locus, common allele with a low penetrance model as might be hypothesized in diabetes from the "thrifty gene hypothesis" [ 56] and other multi-locus models are modest penetrance models for the low frequency alleles.

Thus, since the difference between the non-post-stratified model and the post-strata models were modest, we would rather recommend the non-post-stratified model (which disregards the land use and vegetation types) to be more adequate for most applications that will involve large-scale AGB estimation supported by ALS data, at least until high quality thematic maps are made available.

However, as strong distance and contact restraints have been implemented in the second-round simulation, the topology improvement of the models is modest.

The number of additional iterations required by the Family-Lineage models is modest, increasing by a factor of 1.5, on average, for each additional rate category.

The explained variation in some models is modest, which is unsurprising as the joint effects of independent variables are typically low in HBM applications [ 10].

Whereas the improvement in the models was either statistically significant (comparing health center and nonspatial models) or trending toward significance (comparing spatial and nonspatial models), the absolute differences in the AUCs from spatial and nonspatial models were modest.

Moreover, the analyses may not provide a complete explanation of gut feeling, as the amount of variance explained by the multivariate models was modest (R estimates 0.32 and 0.34).

The neointima formation in all these models is modest and develops slowly over time, with the neointima mainly associated with the stent strut up to 7 days post-procedure.

Addition of ADL measures to these models improved the predictive ability of the models although the effects of ADL measures on the overall predictive ability of the models were modest as measured by changes in the rescaled R and c-statistics.

This suggests that variation in mutation rate is the main predictor of both functional and silent polymorphism in Arabidopsis at these broad spatial scales, although it should be noted that the overall explanatory power of these models is modest for patterns of divergence (table 1) and remarkably low for genome-wide level of polymorphism, where 90% of the variation remains unexplained (table 2).

These results suggest that, globally, the differences produced by each model are modest.