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Record W2087170658 · doi:10.1080/0964056032000133125

Nitrate transport in shallow groundwater at the stream–riparian interface in an urbanizing catchment

2003· article· en· W2087170658 on OpenAlexaffabout
Ryan T. Stainton, Michael Stone

Bibliographic record

VenueJournal of Environmental Planning and Management · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRiparian zoneRiparian bufferGroundwaterNitrateHydrology (agriculture)Environmental scienceSTREAMSUrban streamLand useDrainage basinHydraulic conductivitySoil scienceGeographyEcologySoil waterGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Drive point peizometers were installed at the stream–riparian interface in a small urbanizing southern Ontario catchment to measure the effect of buffers (presence/ absence) and land use (urban/agricultural) on the movement of NO− 3-N in shallow groundwater from the riparian area to the stream. Mean NO− 3-N concentrations ranged from 1.0 to 1.3 mg L−1 with maximum values of 9.4 mg L−1. Holding land use constant, there was no significant difference (p>0.05) in NO− 33-N concentration between buffered and unbuffered sites. Nitrate-N levels were not significantly different (p>0.05) as a function of land use. The lack of difference between sites as a function of buffer absence/presence and land use is probably due to the placement of some peizometers in low conductivity materials that limited groundwater flow from the riparian zone to the stream. Subsurface factors controlling the hydraulic gradient are important in defining buffer effectiveness and buffer zones should not be used indiscrim inately as a management tool in urban and agricultural landscapes to control nitrate-N loading in shallow groundwater to streams without detailed knowledge of the hydrogeo logic environment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.457

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.216
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2003
Admission routes2
Has abstractyes

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