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Many previous studies (Pandey and Soupir [2012a], [2012b]; Kim et al. [2010]; Martinez et al. [2014]; Parajuli et al. [2009]) reported that identification and selection of model input parameters are major challenges when implementing the model for predicting fate and transport of pathogens at the watershed scale.
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Such data are needed to improve the value of numerical models used to predict fate and effects of oil spills and different oil spill responses.
Models provide a useful tool to fill data gaps and to predict fate and effects for compounds which cannot be identified or have not yet been identified using the analytical tools described above.
This study identifies the wide range in possible predictions using existing information and these modeling approaches to highlight the need for further research to ensure that risk assessment, remediation and predicted fate and transport are appropriately presented and interpreted.
Table 11: X denotes predicted fate pattern, which is also used in Table 12 14.
(2) The distribution and variation of individual behavior of predicted fate patterns are not sufficient to understand the features of the fate patterns.
The predicted fate patterns of each genotype derived by using our procedure are collectively called Fate Patterns (JA) (JA for short).
The aims of the simulation are: (i) to investigate predicted fate patterns variations of each genotype, and (ii) to evaluate two rules employed to VPC fate model.
Thus, the VPC fate pattern including the uninterpretable lineage is extended to three predicted fate patterns (see STA block in Figure 1(b)).
It is clear that the number of predicted fate patterns investigated by our method is far smaller than the one summarized by [ 9] (see Table 9).
Finally, we examine the consistency and correctness of the VPC fate model, and evaluate proposed two rules by comparing with three simulation targets consisting of predicted fate patterns obtained from in silico and in vivo experiments.
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