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This could be especially appropriate for the specific BIA dataset, as particularly the range is uncertain.
This analysis does not depend on the additional BIA dataset, and therefore standard sensitivity analyses can be applied.
Again, scenario analyses might be more appropriate to capture the uncertainty in the BIA dataset of the overall BIA model.
The use of standard sensitivity analyses for the BIA dataset might also be limited because of the lack of appropriate distributions as data sources or the need for forecasting.
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Other input data in the BIA model (BIA data) differ from the standard health economic dataset.
Therefore, the quantity ( sqrt{{mathrm{Bias}}^2+{widehat{sigma}}_b^2} ), where Bias is the average prediction bias for the validation dataset, can be interpreted as a measure of prediction accuracy at the relevant scale equivalent to the RMSE used for plot-level predictions.
In addition, the dataset in a BIA model also includes forecasts (e.g. annual growth, uptakes curves, substitution effects, changes in prescription restrictions and guidelines, future distribution of the available treatment modalities, off-label use).
Bias correction requires a historically measured dataset for correction that is at the same grid scale as the spatially downscaled parameter set.
The list of known BIAs (organized by mass group) used to mine the positive mode dataset is found in Additional file 12. Standards were not included as part of the UPLC-FTMS profiling, disallowing absolute quantification.
To gain insight into the BIA content of the 20 selected plant species, we mined the positive ion mode datasets (Additional file 9) and identified exact masses corresponding to empirical formulae of known alkaloids.
BIA consultants can assist in locating, describing, and prioritizing suspects by contributing scientific knowledge and formal analysis of "national datasets and other relevant base rate data" (Rainbow et al. 2011p. 37).
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