Renewable resource management with environmental prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it