Importance of considering the growth response after partial harvesting and economic risk of discounted net revenues when optimizing uneven-aged forest management
Bibliographic record
Abstract
Because of the very high complexity of modern optimization models based on single trees, uncertainties are often disregarded. In this study, we present a modelling approach that allows partial harvesting but is still simple enough to consider risk. Our modelling approach investigates whether the inclusion of timber price uncertainty influences the harvesting schedule. The model considers positive growth response to the density reduction that follows harvesting. Testing the impact of uncertainty, we define the discounted net revenues of each harvest operation as random variables. We compare harvest scheduling both with and without the inclusion of uncertainty. We first model growth response based on a partial-harvest schedule, without integrating uncertainty from timber price fluctuations. The results show that harvesting tree cohorts at different times is financially optimal. We run the same model again, including the risk of timber price fluctuations. The inclusion of risk leads to slightly greater differences in recommended harvest timings. Because of the small difference observed, we conclude that it is unlikely that risk arising from fluctuating timber prices would strongly affect the results for more complex forest economic models concerning the optimal harvest schedules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".