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Record W2130311312 · doi:10.5539/enrr.v2n4p70

Onsite Wastewater System Nitrogen Loading to Groundwater in the Newport River Watershed, North Carolina

2012· article· en· W2130311312 on OpenAlexvenueno aff
Charles Humphrey, Michael O’Driscoll, Michael C. Armstrong

Bibliographic record

VenueEnvironment and Natural Resources Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterWatershedSoil waterEnvironmental scienceLoamHydrology (agriculture)NutrientNutrient pollutionGroundwater pollutionEutrophicationNitrogenGeologyAquiferSoil scienceEcologyChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.019
GPT teacher head0.246
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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