Effects of Nutrient Restrictions on Confined Animal Facilities: Insights from a Structural‐Dynamic Model
Bibliographic record
Abstract
Nutrient emissions from animal feeding operations continue to degrade water and air quality. New regulations will limit the amounts of nutrients that can be locally applied to land. In this paper, a structural‐dynamic model of a livestock‐crop operation is calibrated with data from a representative farm and is used to predict the effects of nitrogen regulations. Policy simulations clarify the importance of dynamic elements and demonstrate three main results: (1) cost estimates for large producers are higher than suggested by previous studies; (2) cross‐media pollution effects are potentially significant; and (3) improved input management appears most promising for reducing both water and air emissions and waste management costs. Implications for policy and future research are discussed. Les émissions d'éléments nutritifs provenant des exploitations d'élevage continuent de dégrader la qualité de l'eau et de l'air. De nouveaux règlements limiteront les quantités d'éléments nutritifs qui pourront être appliqués localement sur les terres. Dans le présent article, un modèle structurel dynamique d'exploitation mixte (élevage‐culture) a étéétalonné d'après des données tirées d'une exploitation typique et a été utilisé pour prédire les répercussions des règlements sur la gestion de l'azote. Des simulations de politiques clarifient l'importance des éléments dynamiques et démontrent trois résultats principaux: 1. l'évaluation des coûts dans le cas des grosses exploitations est plus élevée que celle déterminée dans des études antérieures; 2. les effets de la pollution touchant plusieurs milieux sont potentiellement importants; 3. une meilleure gestion des intrants semble plus prometteuse pour réduire les émissions dans l'eau et l'air et pour réduire les coûts de gestion des déchets. Nous traitons des implications pour la politique et la recherche future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".