Spatial optimization of nutrient reduction measures on agricultural land to improve water quality: A coupled modeling approach
Bibliographic record
Abstract
Abstract The objective of this study is to identify the optimal spatial distribution of Best Management Practices (BMPs) to reduce total phosphorus (TP) runoff from agricultural land in the largest Canadian watershed draining into Lake Erie, the Great Lake most vulnerable to eutrophication. BMP measures include reduced fertilizer application, cover crops, buffer strips, and the restoration of wetlands. Environmental SWAT model results feed into a spatial optimization procedure using two separate objective functions to distinguish between public BMP program implementation costs (PIC) on the one hand and farmers’ private pollution abatement costs (PAC) on the other hand. The latter account for the opportunity costs of land retirement and changing land productivity. PAC are initially lower than PIC but exceed the latter after 30% of the annual TP baseline load is eliminated. This suggests that under optimal conditions existing grant and incentive payments cover the economic costs farmers face up to a maximum of 30% of the baseline load reduction. Imposing further reductions of up to 40% results in a cost to farmers of almost $52 million per year. This is 45% higher than the optimal solution based on PIC and therefore not deemed incentive‐compatible under the watershed's existing cost‐sharing scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".