Linear mixed-effects models and calibration applied to volume models in two rotations of <i>Eucalyptus grandis</i> plantations
Bibliographic record
Abstract
This work presents applications of the linear mixed-effects model calibration to predict individual tree volumes of Eucalyptus grandis W. Hill ex Maiden plantations on first and second rotations located in different farms of the same region in São Paulo, Brazil. We started with the Schumacher and Hall equation in its linearized form to develop our mixed-effects model. Some parameters were considered as random among the different farms, and the calibration was made at the farm level using a small number of sample trees. The approach was developed for univariate models of the first rotation, which were calibrated with first- and second-rotation trees, and for bivariate models of the two rotations, which were calibrated with first-rotation trees. The results showed that the calibrated mixed model provides more reliable predictions than the fixed part of the model alone; however, the benefit is only moderate due to the rather small variation of the stem form between farms and rotations. The results indicate that the approach can reduce the measurement requirements on second-rotation crops.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".