Nonpoint‐Source Pollution Reduction for an Iowa Watershed: An Application of Evolutionary Algorithms
Bibliographic record
Abstract
We apply an integrated simulation‐optimization framework to search for cost‐efficient mix and location of agricultural conservation practices in a typical agricultural watershed for two types of nitrogen reduction targets: control of mean annual nitrogen loadings, and a “safety‐first” type constraint, insisting that nitrogen targets be met in every weather realization (weather‐resilient solutions). Evolutionary algorithms are developed for each of the appropriate water quality targets. Our approach allows for the derivation of a watershed‐level total and marginal nitrogen abatement cost curve. Controlling for the probability of meeting water quality targets (looking for weather‐resilient solutions) is found to be significantly more costly than controlling the average nitrogen loadings. Both types of solutions are assessed for robustness with respect to weather uncertainty: solutions selected to reduce average loadings do well under weather uncertainty, while the robustness of solutions selected to be resilient decreases with the stringency of the water quality goal . Nous avons appliqué un modèle intégré de simulation‐optimisation pour trouver des combinaisons de pratiques agricoles de conservation efficaces en terme de coûts et les endroits où elles devraient être adoptées dans un bassin versant agricole typique pour deux types de cibles de réduction des charges d’azote : la surveillance des charges moyennes annuelles d’azote et une contrainte du type « sécurité d’abord », selon laquelle les cibles d’azote doivent être respectées peu importe les conditions météorologiques (solutions robustes aux changements météorologiques). Nous avons élaboré des algorithmes évolutifs pour chaque cible relative à la qualité de l’eau. Notre approche a permis de dériver la courbe de coût liéà la réduction marginale et totale de l’azote dans un bassin versant. L’ajustement de la probabilité que les cibles relatives à la qualité de l’eau soient respectées (solutions robustes aux changements météorologiques) s’est révélé significativement plus coûteux que de contrôler les charges moyennes d’azote. Nous avons évalué les deux types de solutions afin de vérifier leur performance dans des conditions météorologiques incertaines : les solutions retenues pour réduire les charges moyennes fonctionnent bien dans des conditions météorologiques incertaines, tandis que la performance des solutions robustes aux conditions météorologiques diminue avec le niveau visé de qualité de l’eau .
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".