The role of uncertain forest data in a hierarchical forest planning setting with misaligned objectives
Bibliographic record
Abstract
Forest planning is vital for ensuring objective fulfilment for decision-makers. Forest-owning companies often organise their planning in a hierarchy of separate stages (i.e., strategic, tactical, and operational planning). The objectives for the strategic stage are generally to maximise net present value and long-term harvest levels without threatening the environmental integrity of the forests. However, in the subsequent stages of the planning hierarchy, with a shorter-term focus, the objective is often to minimise costs due to budgetary constraints. These misaligned objectives introduce a dilemma, especially when considering that decisions are typically made using uncertain data. We examined the suboptimality caused by using low-quality forest data in a long-term harvesting planning problem and how this suboptimality is affected by misaligned objectives between the strategic and tactical planning stages. The low-quality forest data were simulated in a Monte Carlo simulation that maintained a real-world structure of errors. The results show that uncertainty in forest data impacts objective fulfilment more than the level of alignment of objectives. However, a high degree of objective alignment performs better than the opposite, regardless of the level of quality of data.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".