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A recent study on optimal sampling layouts (Nowak et al., 2009) showed that a significant portion of uncertainty in predicting plume migration stems from the uncertain total hydraulic flux through the source area.
It is also found that a large portion of uncertainty in the output radiance is driven by the interaction effects among input parameters in the visible (~ 550 nm), whereas the red-near infrared (~ 670 nm), seems to have fewer interaction effects.
For this carbon estimation procedure, the portion of uncertainty attributed to the variance of the probability-based estimator of the population mean using the plot-level predictions is usually correctly estimated, but the portion attributed to the variance of the allometric model estimator is usually ignored.
In this respect, the estimation of reliable confidence intervals for the flux regression coefficients is crucial to remove a large portion of uncertainty in the selection of metabolic targets, considerably improving the odds of successful experimental validation.
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In principle, the interval fuzzy logic approach aims to manage portions of uncertainty that could not be precisiated, leading to improved error performance.
Most of the past research has focused on capturing a portion of the uncertainties: either the uncertainty in project performance analysis or the uncertainty in project decision-making and stakeholder trade-offs.
The issue is that the portion of the uncertainty region that is perceived at a given moment is determined by the labels known at that moment.
The benchmark has been named the OECD/NEA UAM-LWR benchmark, and has been divided into three phases each of which focuses on a different portion of the uncertainty propagation in LWR multi-physics and multi-scale analysis.
We performed an in-depth analysis of the contributing factors to Usystem and found that a large portion of the uncertainty is the result of physical system stability.
Under these circumstances and while we do not have labeled instances covering all classes, the uncertainty region perceived by the active learner (Fig. 1a) is reduced to a portion of the real uncertainty region (Fig. 1b).
However, improved understanding about cohort mortality effects and life-years lost within epidemiological studies would greatly inform valuation, with a significant portion of the remaining uncertainty related to a value judgment about whether health outcomes should be valued on a life-year lost or willingness to pay basis.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com