The respective ranks for the roots of the 'child' variables are the ranks of the respective roots of the parent variables minus 1.
The nodes at the root of this graph are primarily environmental variables (in the present case external metabolites).
Given the importance of climate variables in driving other belowground processes such as coarse root decomposition [22], the integration of these variables in models for describing other belowground stocks (e.g., dead roots) may not only aid in understanding C stock differences across large geographic scales but may also inform our understanding of other belowground processes.
In the next section, we locate the root of Peirce's introduction of quantifiers and bound variables.
Our study shows that the reconstructed genetic network regulating metabolism in E. coli is hierarchical, modular, and largely acyclic, with environmental variables controlling the root of the hierarchy.
The first alternative starts with a variable, which is the root of the tree (or sub-tree).
Given the importance of climate variables in driving other belowground processes such as coarse root decomposition [ 22], the integration of these variables in models for describing other belowground stocks (e.g., dead roots) may not only aid in understanding C stock differences across large geographic scales but may also inform our understanding of other belowground processes.
Given this expected inverse square root relationship between C f and mirror surface area, we transformed mirror surface area by inverting then taking the square root of the variable before adding it as a factor to the linear model.
Percentage data were arcsine transformed (calculating arcsine of the square root of the variable) prior to analyses.
Pareto scaling involves weighting each of the variables by the square root of that variable's variance, minimizing the impact of noise and increasing the importance of low-concentration metabolites in the subsequent analysis.
For large values, this Bessel function is a negative exponential function of the square root of the random variable.
