Renewable resource management with environmental prediction
Bibliographic record
Abstract
Variations in environmental conditions affect renewable resource growth. The ability to predict such variations is improving, providing scope for improved management. We generalize a common stochastic stock recruitment model to explore how optimal management changes with environmental prediction. We obtain three main results. First, while it might seem that a prediction of adverse future conditions should lead to more conservative management, the opposite may be true. Second, optimal management requires only a one‐period‐ahead forecast, suggesting forecast accuracy is more important than forecast lead time. Finally, we derive conditions on environmental fluctuations guaranteeing positive optimal harvest in every period. Gestion d'une ressource renouvelable quand on prédit les conditions futures de l'environnement. Les variations dans les conditions de l'environnement affectent la croissance de la ressource renouvelable. La capacitéà prévoir ces variations s'améliore et ouvre la possibilité d'améliorer la gestion de la ressource. Les auteurs utilisent un modèle de ressource renouvelable avec croissance stochastique et obtiennent trois résultats. D'abord, alors qu'il peut sembler que des prévisions pessimistes de conditions difficiles dans l'avenir peuvent conduire à une gestion plus conservatrice, le contraire peut être vrai. Ensuite, la gestion optimale requiert seulement une prédiction pour la prochaine période: voilà qui suggère qu'il est plus important d'avoir une prévision exacte que d'avoir des prévisions à plus long terme. Enfin, on développe les conditions pour les fluctuations de l'environnement qui garantissent une récolte positive optimale à chaque période.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".