FIELD SCALE MODELING TO ESTIMATE PHOSPHORUS AND SEDIMENT LOAD REDUCTIONS USING A NEWLY DEVELOPED GRAPHICAL USER INTERFACE FOR SOIL AND WATER ASSESSMENT TOOL
Bibliographic record
Abstract
Streams throughout the North Canadian River watershed in northwest Oklahoma, USA have elevated levels of nutrients and sediment. Soil and Water Assessment Tool (SWAT) was used to identify areas that likely contributed disproportionate amounts of Phosphorus (P) and sediment to Lake Overholser, the receiving reservoir at the watershed outlet. These sites were then targeted by the Oklahoma Conservation Commission (OCC) to implement conservation practices, such as conservation tillage and pasture planting as part of a US Environmental Protection Agency Section 319(h) project. Conservation practices were implemented on 238 fields. The objective of this project was to evaluate conservation practice effectiveness on these fields using the Texas Best Management Evaluation Tool (TBET), a simplified Graphic User Interface (GUI) for SWAT developed for field-scale application. TBET was applied on each field to predict the effects of conservation practice implementation on P and sediment loads. These predictions were used to evaluate the implementation cost (per kg of pollutant) associated with these reductions. Overall the implemented practices were predicted to reduce P loads to Lake Overholser by nine percent. The ‘riparian exclusion’ and ‘riparian exclusion with buffer’ practices provided the greatest reduction in P load while ‘conservation tillage’ and ‘converting wheat to bermuda grass’ produced the largest reduction in sediment load. The most cost efficient practices were ‘converting wheat to bermuda grass’ or ‘native range’ and ‘riparian exclusion’. This project demonstrates the importance of conservation practice selection and evaluation prior to implementation in order to optimize cost share funds. In addition, this information may lead to the implementation of more cost effective practices and an improvement in the overall effectiveness of water quality programs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".