Quantifying the Environmental Benefits of Conserving Grassland
Bibliographic record
Abstract
The Missouri River Basin (MRB) functions as the “life zone” for the larger Mississippi River Basin, providing grassland habitat that infiltrates precipitation and recharges groundwater, reduces sediment erosion, filters nutrients, stores carbon, and provides critical habitat for wildlife. The role of this region as a producer of food and fuel, both nationally and internationally, creates unique challenges for conservation. To support conservation efforts and sustainable management of this invaluable resource, a large-scale, screening-level evaluation of the water quantity and quality benefits of land conservation efforts in the MRB was performed. This paper describes the development and application of a Soil and Water Assessment Tool (SWAT) model to the MRB study area to provide estimates of water quantity and quality (sediment, total phosphorus, total nitrogen) benefits from the avoided conversion of intact grassland to cultivated cropland. The results of this study indicate that the avoided conversion of grassland to cropland could potentially prevent more than 1.7 trillion gallons of surface runoff as well as prevent the export of approximately 46 million tons of sediment, 87 million pounds of total phosphorus, and 427 million pounds of total nitrogen from the MRB study area landscape every year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".