Onsite Wastewater System Nitrogen Loading to Groundwater in the Newport River Watershed, North Carolina
Bibliographic record
Abstract
The objectives of this research were to calculate the on-site wastewater system (OWS) nitrogen loading to groundwater in the Newport River watershed, North Carolina and determine if these loads were large enough to be included in watershed nutrient management plans along with other nutrient sources such as row-crop agriculture. Nitrogen loadings were calculated using hydrological and groundwater quality data beneath 16 OWS installed in three different soil groups, and watershed demographic and soil data. Over 30,000 people use OWS in the watershed with 76% of the systems installed in group I soils (sands), 11% in group II soils (sandy loams), and 13% in group III soils (sandy clay loams). OWS in group III soils had lower total dissolved nitrogen loading rates (0.04 kg/person/yr) to groundwater than systems in group I (1.41 kg/person/yr) and II soils (0.33 kg/person/yr). The total dissolved nitrogen loading rates from OWS to groundwater, assuming 20 people/ha in group II and I soils (6.5 to 28.1 kg/ha/yr), were significant, but less than potential agricultural contributions to groundwater (37.5 kg/ha/yr) for the area. OWS are significant sources of shallow groundwater nitrogen loading in coastal watersheds with sandy soils, and these contributions should be considered in regulatory efforts to reduce nutrient pollution.
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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) | 0.000 | 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 | 0.001 |
| 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".