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It is not straightforward to see where in the mathematical core of energy models input variables are processed, and hence, tracing back results to individual inputs could be difficult.
Apart from the parameter values derived from published literature, a workshop with 10 experts representing members of the Schmallenberg surveillance team at the Animal Health Veterinary Laboratories Agency, industry representatives, veterinary clinicians and academic researchers was held to present and discuss the structure of the production models, input variables and assumptions.
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Uncertainty and variability in model input variables were represented using probability distributions, and uncertainty in the model output was estimated through statistical simulation.
An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale.
It may occur in practice that some model input variables and parameters can be represented by probability distributions (due to observed variability and sufficient statistics), while others are better represented by possibility distributions (due to imprecision), or by the Dempster Shafer belief functions (due to partial observed variability and partial ignorance).
We also estimated uncertainties of the model input variables based on values reported in the literature.
Notably, the model input variables can be easily calculated for non-specialist user.
Results of applying BMA to energy model input variables are PMPs of competing models for input variables of an energy model.
The ANN model input variables are: CPT tip resistance (qc), effective vertical stress (σ′v), and CPT sleeve friction (fs).
This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway.
Sensitivity analysis was carried out to examine the relative changes in the flow with respect to change in selected model input variables.
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