Sentence examples for sensitive estimation from inspiring English sources
Although the use of artificial neural networks (ANNs) has allowed significant progress in the estimation of suspended loads, there is still a need for more sensitive estimation methods.
The qrt-PCR is a powerful technique able of accurate and sensitive estimation of microbial species abundance in different environments for applied ecology studies.
This study which is the largest reported thus far, implemented an active home based surveillance for morbidity and had a follow up of full one year which potentially provided sensitive estimation of the morbidity.
The regression trees, built on data from the two study regions, provided a temporally sensitive estimation of complex hierarchical interactions between the predictors.
Despite the possible contamination, among all state-of-the-art methods, the Taxy-Pro approach showed by far the most sensitive estimation of viral DNA abundances and therefore our method can significantly contribute to quality control in microbial and viral metagenomics.
Recent investigations of the global distribution of biomass within forests have provided insight for comparing size- and species-specific predictions (i.e., allometric equations and proportional ratios) with climate-sensitive estimations of belowground C [e.g., 8].
This suggested that the estimates of year and Mutuelles are not sensitive to estimation methods.
However, the available techniques are sensitive to estimation error of the AIR, which is difficult to estimate in realistic environments.
In an effort to improve on these current limitations we have engineered an alternative and possibly more sensitive 'seed' estimation method that utilises a Bayesian likelihood approach to estimate the probability that a 'seed' motif is over-represented within a differentially expressed gene profile.
It also does not appear very sensitive to estimation type or corrections for serial correlation.
Under unstable cases, it was shown that the performance was sensitive to estimation of the roughness sub-layer depth.
