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Accurate monitoring of uncertainty in medical decision making and accurate assessment of one's own knowledge and skills is required to recognise and follow up on potential problems.
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In the private sector, tight schedules commonly challenge appropriate monitoring of uncertainties (Pavie et al., 2014).
The monitoring strategy is based on groundwater monitoring effort, reduction of uncertainties, socioeconomic needs, legal requirements, etc.
In addition to surveying current use of DNA-based tools for aquatic invasive species monitoring, we explore potential sources of uncertainty associated with molecular technologies and possibilities for limiting that uncertainty and effectively communicating its implications for decision-making.
Our results demonstrate the computational efficiency of our approach and its ability to quantify the impact of the design of the monitoring network on the reduction of uncertainty associated with the characterization of a migrating contaminant plume.
Spatial variation in ACD is also the largest source of uncertainty in monitoring carbon emissions for voluntary carbon offset markets and for developing international action for Reduced Emissions from Deforestation and Forest Degradation (REDD) at national and sub-national levels [1 4].
However, in most cases it is not possible to estimate all relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertainty components that can be quantified will not be well determined due to the lack of replication at different levels of the random variations (e.g. number of stations, number of years, and number of people).
Related to the dominant theme of health monitoring opportunity, was the theme of uncertainty about the seriousness of existing health concerns.
Spatial variation in ACD is also the largest source of uncertainty in monitoring carbon emissions for voluntary carbon offset markets and for developing international action for Reduced Emissions from Deforestation and Forest Degradation (REDD) at national and sub-national levels [ 1- 4].
Models should calculate estimates of uncertainty by monitoring fitting and χ; if the fit is poor for a given pixel or ROI, this should be labelled as a model failure and excluded from further analysis (e.g. ROI mean or histogram).
The disadvantage of the uptake monitoring, the uncertainty about the localization of the chemical interacting with the cell either on the cell membrane or inside the cell, can be alleviated using the SBSP models (see sections −).
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