Spatial prediction of optimal final stand density for even-aged plantation forests using productivity indices
Bibliographic record
Abstract
Surfaces that describe spatial variation in optimal stand density following final thining (Sopt) are likely to be of considerable use to forest managers. Using a comprehensive series of growth model simulations, the aim of this research was to (i) develop a model of Sopt that maximises volume of large-diameter, small-branched sawlogs (S27) for unpruned New Zealand radiata pine (Pinus radiata D. Don) stands, (ii) use this model to examine how site productivity and tree morphology influence Sopt, and (iii) generate a map of Sopt for New Zealand. A model predicting Sopt from clearfell age and two productivity indices, Site Index (SI) and 300 Index (I300), was found to predict optimal stand density with a high degree of accuracy. Optimal stand density was found to increase with I300 and clearfell age but decrease with SI. Within New Zealand plantations, the mean predicted Sopt for clearfell age 28 years was 614 stems·ha−1. The proportion of plantations predicted to have Sopt greater than 400, 500, and 600 stems·ha−1 was 0.99, 0.88, and 0.61, respectively. The predicted Sopt was found to exceed the actual mean final crop stand density in stands managed under unpruned sawlog regimes of ca. 500 stems·ha−1 within most plantation areas in New Zealand. This disparity highlights the potential of this approach for increasing crop value in New Zealand P. radiata plantations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".