Forest management optimization in <i>Eucalyptus</i> plantations: a goal programming approach
Bibliographic record
Abstract
In Galicia (Spain), many Eucalyptus plantations are managed using the area-control method. The ultimate goal is to guarantee an even flow of wood in perpetuity by reaching the normal age-class distribution of the fully regulated forest by the end of a given planning horizon. However, given that the productivity of coppice stands differs throughout the successive rotation intervals, the application of this method triggers excessive fragmentation of the forest area. We present a model with the same long-term goal that does not force plantations into any given final age-class distribution. The model permits the plantations to reach a final structure with fewer harvest units of larger average size. To illustrate this approach, we developed two models and applied them to a case study. The first model used the principle of area control to achieve the fully regulated structure in each site and rotation interval of one full plantation cycle. The second model guaranteed a constant yield beyond the planning horizon without imposing any specific final age distribution on the plantation area. Both models considered objectives such as a constant yield during the planning horizon and the net present value of harvests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".