A climate change adaptive dynamic programming approach to optimize eucalypt stand management scheduling: a Portuguese application
Bibliographic record
Abstract
The aim of this paper is to present approaches to optimize stand-level, short-rotation coppice management planning, taking into account uncertainty in stand growth due to climate change. The focus is on addressing growth uncertainty through a range of climate scenarios so that an adaptive capacity may be possible and the vulnerability of the stand to climate change may be reduced. The optimization encompasses finding both the harvest age in each cycle and the number of coppice cycles within a full rotation that maximize net present revenue. The innovation lies in the combination of the process-based model (Glob3PG) with two dynamic programming (DP) approaches. The former is able to project growth of eucalypt stands under climate change scenarios. The innovative approaches are thus influential to define the management policy (e.g., stool thinning, number of coppice cycles, and cycle length) that maximizes net present revenue taking into account uncertainty in forest growth due to climate change. In both approaches, the state of the system is defined by the number of years since plantation, whereas DP stages are defined by the cumulative number of harvests. The first approach proposes the optimal policy under each climate change scenario at each state. The second approach addresses further situations when the climate scenario is unknown at the beginning of the planning horizon. Both help address uncertainty in an adaptive framework, as a set of readily available options is proposed for each scenario. Results of an application to a typical Eucalyptus globulus Labill. stand in central Portugal are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".