Implementing the conditional value at risk approach for even-flow forest management planning
Bibliographic record
Abstract
Deciding on a plan of action for a forest holding involves a significant amount of uncertainty. As forest planning involves the use and extraction of resources, uncertainty lies in both the future development of the forest (biological uncertainty) and the development of the market for forest-based products (economic uncertainty). Additionally, natural hazards can be a source of unexpected losses. In traditional forest management planning, the most common way to deal with uncertainty is to ignore it. Growth models are used that are assumed to be correct, and timber prices are assumed to be held constant. By ignoring the fact that these models provide only one representation of what may happen, the forest owner may get an overly optimistic (or pessimistic) view of the potential value of the forest holding. Through a stochastic programming formulation, these uncertainties can be modelled directly into the optimization formulation, and a management plan can be created that incorporates the risk preferences of the decisionmaker. This is highlighted through an example that maximizes the net present value of the holding while minimizing the conditional value at risk of obtaining a stated even flow of income.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".