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This paper first describes a shallow water table model, based on deforming finite element (DFE) framework, to characterize the near-surface field-to-field hydrologic response to various irrigation and drainage management regimes along a gently sloping alluvial fan.
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A generalized water table fluctuation model based on precipitation was developed using a statistical conceptualization of unsaturated infiltration fluxes.
For example, for a woman 180 cm tall compared to the population average of 165 cm (see online supplementary table S1), the model based on height as a continuous variable predicts a 14% increase in risk.
We present the results in Table 6 with EvoLabelPred model based on K = 3 clusters and conditional probabilities-based label prediction.
As shown in Table 1, the decision model based only on motifs detected above had the worst distinguishing power, with an average accuracy of 75.6%.
Pixels that belong to the table can be extracted from these coordinates with a mathematical model based on table geometry, provided that this model is explicitly defined.
Table 5 shows the initial model based on J-SHIS.
As we can see from Table 4, this novel identification model based on the basic genetic algorithm has a relatively higher precision: the relative error between identification results of pollutant amount and analytic results varied from −1.7 to 5.0 %, and the relative error between identification results of source position and analytic results varied from 0 to −1.6 %.
Standard measures (Table 1) show that the model based on the Recursive Partitioning classifier outperforms the other five models (74% sensitivity, 48% specificity and 65% accuracy; rpart/marker).
The basis of the model (Version 1 of the instrument; Table 1) was the initial model, based on published evidence from a literature review on characteristics of children with PPC needs.
In addition, climatic conditions were obtained for each population of origin (Additional file 3: Table S3), using an extrapolation model, based on 1971 2000 measurements from nearby weather stations, and implemented in BioSIM [ 86, 87].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com