Agricultural Drainage and Nitrate Transport to Streams in the Humid Region of North America
Bibliographic record
Abstract
Subsurface drainage is used extensively in the Midwest U.S. and certain areas in the eastern U.S. and Ontario and Quebec in Canada to improve crop production. Approximately 25% of the cropland in the U.S. and Canada requires drainage. Research beginning in the 1970s started to show that agricultural drainage has significant impacts on surface water quality. Subsurface drainage reduces surface runoff, sediment losses, and the movement of contaminants attached to the sediment into surface waters, but increases the losses of nitrogen. Nitrogen losses from intensively drained cropland in the Midwest are considered a major contributor to excessive nitrogen and hypoxic conditions in the Gulf of Mexico. During the 1980s and 1990s, there were numerous research reports in the U.S. and Canada on the impact of agricultural drainage on water quality. By the late 1990s, the development of methods to reduce losses of nitrogen in drainage waters has become a primary objective in addressing the environmental impacts of agricultural drainage for researchers and engineers. Reducing nitrogen losses is difficult because the nitrate form is mobile in soil solution and may be readily leached with subsurface drainage water. A number of methods may be used to reduce losses. They include source reduction by fertilizing at appropriate rates and times, cover crops, routing drainage water through wetlands, use of biofilters, and drainage water table management (DWM), also known as controlled drainage (CD). Controlled drainage to reduce nitrates in the humid region was first reported from North Carolina in the literature in the late 1970s.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".