Assessment of Nitrogen Inputs into Hunt River by Onsite Wastewater Treatment Systems via SWAT Simulation
Bibliographic record
Abstract
Nonpoint source nitrogen pollution is difficult to effectively model in groundwater systems. This study aims to elucidate anthropogenic nonpoint source pollution discharging into Potowomut Pond and ultimately Narragansett Bay. Hydrologic modeling with Soil and Water Assessment Tool (SWAT) and SWAT Calibration and Uncertainty Program (SWAT-CUP) was used to simulate streamflow and nitrogen levels in the Hunt River with and without onsite wastewater treatment systems (OWTS). The objective of this study was to determine how input of OWTS data impacts nitrogen loading into the Hunt River Watershed in Rhode Island, USA. The model was simulated from 2006 to 2014, calibrated from 2007 to 2011 and validated from 2012 to 2014. Observed streamflow data was sourced from a US Geological Survey gauge and nitrogen loading data from University of Rhode Island Watershed Watch (URIWW). From the results, adding OWTS data to the SWAT simulation produced a better calibration and validation fit for total fit (Nash–Sutcliffe Efficiency (NSE) = 0.50 calibration, 0.78 validation) when compared with SWAT simulation without OWTS data (NSE = −1.3 calibration, −6.95) validation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".