Sustainable management of Great Lakes watersheds dominated by agricultural land use
Bibliographic record
Abstract
Runoff of agricultural nutrients and sediments has led to re-eutrophication of lakes and impaired stream health in the Great Lakes Basin since around 2000 following earlier success in protecting water quality. Substantial investment in conservation actions has had insufficient impact, due in part to a limited basis for understanding the likely environmental outcomes of those investments. This article introduces a special section focusing on promoting investment that produces environmental outcomes as opposed to investing in conservation actions with unknown effects. The special section contains articles in three main categories: 1) studies based on fine-grain SWAT and other simulation modeling that can guide the type, amount, and location of conservation investments to increase their environmental impact; 2) edge-of-field measurement studies that provide updated knowledge to assist in further refining models to increase their predictive power; and 3) articles presenting innovative approaches to incentivizing outcome-oriented conservation investment. Implementation approaches discussed include certifying private crop nutrient advisors as recommending only appropriate timing, amount, and placement of nutrients; working within the existing public drain management system to incentivize conservation; and others. The special section shows that advances in SWAT modeling provide a powerful basis for targeting conservation investments to protect water quality in the Great Lakes Basin, while also demonstrating opportunities to further refine the models. It illustrates both the opportunity and the need to engage in more innovative institutional design of agricultural management programs that go beyond the traditional government programs and do more to reward outcomes and not just actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".