How should we sustain future forests under extreme risk?
Bibliographic record
Abstract
In this paper, we examine the implications of managing for sustained yield in a world characterized by growing risk and uncertainty. We review the history of sustained yield (SY) forestry in North America, with an emphasis on economic benefits and the persistence of the SY paradigm today, despite a publicized shift towards managing for a wider range of forest values called sustainable forest management (SFM). We show that current forest management goals around sustainability as well as SFM indicators are still predicated on maximizing harvest levels and timber flows. We build a simple model to explore the implications of SY under extreme (fat-tailed) risk assumptions to show that maximizing a level of harvest without adequately accounting for risk leads towards the depletion of the forest stock with a corresponding decline in the forest economy. We discuss these results in relation to real-world events such as the increase in catastrophic fires and pest outbreaks like the mountain pine beetle in Western Canada. We then examine the theoretical and practical implications that flow from this model and analysis.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".