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The 82Rb K 1 estimates and 15O-water k 2 estimates from AIFs were compared to those from β AUC scale-corrected IDIFs by linear Deming regression, which models error in both variables, and by the Lin concordance coefficient [24], which provides a measure of absolute agreement between two estimates.
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The conclusions highlight the importance of incorporating the model error in the calibration.
Uncertainties denote the operators which describe data error, numerical error and model error in the mathematical methods.
Figure 5 Weighted sum of the magnitude of the parameters versus the model error in a three-carrier simulation.
Comparing with (15), we observe that the model error in the estimation (16) is much smaller if.
The residual model error in the linear models fit was similar across treatments for live trees using all plots (Tables 2, 3).
With the un-weighted linear models the relative model error in the average tree-level estimate of AGB is 7 12%% (column ACT in Table 4).
As shown in [6], the linear objective function reflects the mean-squared deviation of from, that is, the model error in the TF domain.
There was no clear trend for the effect of climate on model error in comparisons of the model with stress processes removed.
We also propose a simple approach to take into account the presence of the model error in the framework of the EKF-AUS-NL.
We show that the proposed algorithm enables us to give differentiated predictive uncertainty bounds which represent model error in more realistic ways.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com