Modelling and prediction of dominant height and site index of Eucalyptus globulus plantations using a nonlinear mixed-effects model approach
Bibliographic record
Abstract
A nonlinear mixed-effects model approach was used to model dominant height and site index for Eucalyptus globulus Labill. plantations in southeastern Australia. Mixed effects were considered initially for all three parameters of a modified Chapman–Richards model. Inclusion of random effects improved fitting and accounted for the within-plot heteroscedasticity. To correct for within-plot autocorrelation, a power autocorrelation model allowing for irregular intervals for remeasurements was found to be most appropriate. Additional fertilizer application at age 1 year and a number of environmental variables were related to the fixed-effects parameters, but these were not statistically significant, whereas mean annual rainfall and average daily maximum temperature in July (winter) greatly reduced the residual variability among plots. The resulting nonlinear mixed-effects model combines dominant height and site index prediction into a single model and predicts polymorphic height growth rates on different sites. The model can be used to predict population-mean dominant heights and site indices for different growing conditions of E. globulus plantations using existing information of annual rainfall and daily maximum temperature. When prior measures of dominant heights at several ages are available for a plot, specific random effects can be estimated and localized predictions of dominant height or site index can be obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".