Sentence examples for models predicting wood from inspiring English sources

Aspect, on the other hand, was highly significant in models predicting wood density.

Models for predicting wood properties, specifically stress-wave velocity and wood density, also require stand density to be taken into account.

The slopes of linear regression models between actual and predicted wood densities within each trial, along with their 95%% confidence intervals.

The parameter that best validates the performance of the model is the slope of the linear regression model between actual and predicted wood densities within each trial.

When the model is applied to predict wood density for a particular site it is calibrated using this local parameter L. This parameter can be obtained using either the wood density index map (Palmer et al. 2013) or a wood density measurement from a sample of trees of known age (see Appendix 1 for details).

The models were then used to predict wood properties over an extended time horizon, extrapolated beyond the data range and applied to forecast the age at which production of timber of specific dimensions and stiffness ratings would be possible.

A national model of radiata pine wood density, which predicts wood density at breast height as a function of ring number from the pith and a "local parameter", was modified to account for the effects of genetic improvement.

The wood density model in Forecaster (Kimberley et al. 2015a) uses this Density Index to predict wood density of logs cut from each stem, taking into account factors such as felling age and management regime (e.g. stocking), with genetic adjustments using GF Plus density rating as described in this paper.

Other models predict no effects.

Models predict trends in climate, not particular events.

Game theory models predict it, real-life examples confirm it.