Sentence examples for model sensitivity of from inspiring English sources
For RoMANS, a new ensemble technique was employed in which input parameters were varied to explore the range, variability, and model sensitivity of source attribution results and statistical measures of model fit over thousands of trials for each set of concentration measurements.
Seasonal patterns in the model sensitivity of for instance PAR, NIR and FIR emission coefficients of the cover indicate that a greenhouse with adjustable cover parameters will be advantageous over a design with fixed greenhouse cover parameters, as is usually implemented.
The sensitivity and specificity of the radiologist A subclassification for significant cancer were 0.14 and 0.54, respectively (c.f. LR model sensitivity of 0.71 and specificity of 0.61).
The sensitivity and specificity of radiologist B sub-classification for significant cancer were 0.40 and 0.34, respectively (c.f. LR model sensitivity of 0.50 and specificity of 0.76).
Due to potential differences in the properties of the independent variables included in the generalized linear model, sensitivity of the model fit to selection of other potential family and link function combinations was performed.
In animal models, sensitivity of dopaminergic receptors has been augmented by levodopa, with a nociceptive effect that was depressed by blockage of D1 receptors [10].
Supplementary Material Section 1 includes details of model sensitivity to the R0 range.
Despite the acknowledged limitations of this model, sensitivity testing of variables over a wide range of relevancy shows results that are clinically plausible.
Finally, based upon the validated model, sensitivity analysis of injection temperature is conducted.
Although the above modeling strategy has been widely applied and validated in various contexts (23, 24), limitations in data and model sensitivity are of major concern.
The Sobol method (Sobol 1993, 2001) is a variance-based method that performs a quantitative analysis of model sensitivity based on the principle of variance decomposition.
