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For the torque testing data, root mean squared-error (RMSE), fraction of variance (R2) and mean absolute percentage error (MAPE) were found to be 0.9017%, 0.9920% and 7.2613%, respectively.
In the analysis of pole data, root presence decreased with increasing width (P < 0.001), being 87, 77, and 50%% for 0 < 0.5, 0.5 < 1.0, and ≥1.0 m, respectively, and this trend was not influenced by other factors.
In order to determine the deviations between all calculated and experimental data, root mean square deviation (σRMSD) was calculated as follows: σ RMSD = 1 n ∑ i = 1 n P Exp − P Cal P Exp i 2 0.5 × 100.
For these data, root mass and root length in each diameter class were analysed separately to determine the effect of depth (0 < 0.5, ≥ 0.5 m) and width (0 < 0.5, 0.5 < 1.0, ≥1.0 m).
The biomass data (root and shoot dry weight) of the plant individuals were averaged per species and mesocosm in order to prevent pseudoreplication.
The model fit statistics suggested a reasonable fit to the data (root mean square error of approximation =.09, comparative fit index =.87).
Similar(52)
The dependence of experimental and model data root-mean-square deviation on the F10.7 index is shown to be nearly linear.
Three statistical analyses were used to compare transcript FC detected by each of the three methods to the gold-standard qPCR data: root-mean-square deviation (RMSD) for genes with at least two FC in expression, Receiver Operator Characteristic curves (ROC curve) with their associated Area Under the Curve (AUC) value, and Matthew's correlation coefficient (MCC).
One specific need is for new data on root biomass, root production, and the partitioning of belowground respiration into root and soil components.
The observational data base is rooted on prospective data collected on an unselected group of patients with continuous surveillance of outcome events.
Data on root characteristics such as number of roots per plant, root volume, roots dry weight, maximum root length and root thickness were recorded.
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