An integrated economic-hydrologic modeling framework for the watershed evaluation of beneficial management practices
Bibliographic record
Abstract
This paper develops an integrated economic-hydrologic modeling framework that includes an on-farm economic model, a farmer adoption behavior model, a watershed modeling toolbox and a nonmarket valuation economic model for watershed evaluation of beneficial management practices (BMPs). The modeling framework is specifically designed for the watershed evaluation of BMPs (WEBs) project, which was established as a partnership between Agriculture and Agri-Food Canada and Ducks Unlimited Canada in 2003. The modeling framework could be applied to examine tradeoffs between on-farm costs, pollution abatement, water quality improvement, and societal value in evaluating agricultural conservation programs. In comparison with previous studies, the modeling framework not only integrates economic and physical models but also incorporates normative and behavioral elements in addressing the complex conservation issue. This study introduces a modeling framework for policy-makers and program managers to improve the design and implementation of conservation programs to meet the environmental and ecological goals more cost-effectively.
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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 | 0.002 | 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 | 0.000 |
| 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 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".