OPTIMIZATION OF RIPARIAN ZONE NITROGEN MANAGEMENT THROUGH THE DEVELOPMENT OF RIPARIAN MODEL
Bibliographic record
Abstract
This thesis addresses the modeling approach to benefit the riparian zone nutrient management related to water quality in the Northeast and Midwest of USA. Nutrient (primarily Nitrogen (N)) loss from agricultural watersheds through runoff and drainage water continues to be a water quality concern of global importance. Since N is a crucial input for the sustainability of agriculture, the use of N has increased dramatically in recent decades and the excessive nutrient losses have increased too. Like global concern, agriculture (cropland, pasture, managed forest) is an important component of many watersheds of the USA Northeast where N flux to major estuaries is of substantial concern. In this circumstance, the finding from almost 30 years of research on riparian zone hydrology and biogeochemistry demonstrates that riparian zones can serve as best management practices (BMPs) to minimize the adverse agricultural impact on water quality. Riparian zones have been used as one of the most important practices for water quality improvement in agricultural settings due to its ability to perform multi functions including reducing NO3- concentrations in subsurface flow, trapping sediments and pesticides in overland flow, and control erosion. They are often characterized as “filters” or “buffers” and are vital elements in watershed management schemes for water quality maintenance and stream ecosystem habitat protection. Nevertheless, the buffering capacity of riparian zones (mostly for N) varies enormously due to the hydrogeomorphic setting such as topography, soil type, and surficial geology of the riparian zone. Upland land use/land cover affects both the water quantity and quality of the water entering the riparian zone. Hydrogeomorphic setting can influence the flowpaths and hydrologic connections be-tween upland sources of nitrate and the biologically active (i.e., upper 1-2 m) portions of the riparian zone. Thus, a number of key attributes related to location are critical in determining the potential impact of a riparian zone on water. These attributes are incorporated in models like the Riparian Ecosystem Management Model (REMM; Altier et al., 2002; Lowrance et al., 2000). Given the interest in expanding riparian zone BMPs, there is a critical need to advance our understanding of riparian functions at the site scale. Sitespecific models can improve riparian zone management decisions that seek to place, restore and protect riparian zones more effectively. REMM has been used to simulate managed riparian ecosystems in a number of settings in USA including Chesapeake Bay Watershed, Delaware, Mississippi, North Carolina, Georgia, California, and Puerto Rico.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".