Dynamic Programming and Learning Models for Management of a Nonnative Species
Bibliographic record
Abstract
Nonnative invasive species result in sizeable economic damages and control costs. Because dynamic optimization models break down if controls depend in complex ways on past controls, nonuniform or scale‐dependent spatial attributes, etc., decision‐support systems that allow learning may be preferred. We compare two models of an invasive weed in California's grazing lands: (i) a stochastic dynamic programming model and (ii) a reinforcement‐based, experience‐weighted attraction (EWA) learning model. We extend the EWA approach by including stochastic forage growth and penalties for repeated application of environmentally harmful controls. Results indicate that EWA learning models offer some promise for managing invasive species. Les espèces non indigènes envahissantes entraînent des dommages économiques et des coûts de lutte considérables. Compte tenu que les modèles d'optimisation dynamique échouent lorsque les moyens de lutte dépendent, de façon complexe, de moyens de lutte antérieurs, d'attributs spatiaux influencés par l'échelle ou non uniformes, etc., l'utilisation de systèmes d'aide à la décision permettant l'apprentissage pourrait être préférable. Nous avons comparé deux modèles dans le cas d'une plante adventice envahissant les pâturages de la Californie: 1. un modèle de programmation dynamique stochastique; 2. un modèle d'apprentissage experience‐weighted attraction (EWA), fondé sur le renforcement. Nous avons élargi le modèle EWA en y incluant la croissance stochastique des fourrages et des pénalités imposées pour l'utilisation répétée de moyens de lutte dommageables pour l'environnement. Selon les résultats obtenus, les modèles d'apprentissage EWA semblent prometteurs pour la gestion des espèces envahissantes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".