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Record W2077865655 · doi:10.1021/es060724w

Decadal Geochemical and Isotopic Trends for Nitrate in a Transboundary Aquifer and Implications for Agricultural Beneficial Management Practices

2006· article· en· W2077865655 on OpenAlexaff
Leonard I. Wassenaar, M. Jim Hendry, Nikki Harrington

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNitrateAquiferEnvironmental scienceGroundwaterNutrient managementManureFertilizerAgricultureWater qualityNutrientContaminationHydrology (agriculture)Water resource managementEnvironmental chemistryAgronomyEcologyChemistryGeology

Abstract

fetched live from OpenAlex

Nitrate contamination of aquifers is a global agricultural problem. Agricultural beneficial management practices (BMPs) are often promoted as a means to reduce nitrate contamination in aquifers through producer optimized management of inorganic fertilizer and animal manure inputs. In this study, decadal trends (1991-2004) in nitrate concentrations in conjunction with 3H/3He groundwater ages and nitrate stable isotopes (delta15N, delta18O) were examined to determine whether BMPs aimed at reducing aquifer-scale nitrate contamination in the transboundary Abbotsford-Sumas aquifer were effective. A general trend of increasing nitrate concentrations in young groundwater (< approximately 5 yr) suggested that voluntary BMPs were not having a positive impact in achieving groundwater quality targets. While the stable isotope data showed that animal manure was and still is the prevalent source of nitrate in the aquifer, a recent decrease in delta15N in nitrate suggests a BMP driven shift away from animal wastes toward inorganic fertilizers. The coupling of long-term monitoring of nitrate concentrations, nitrate isotopes, and 3H/3He age dating proved to be invaluable, and they should be considered in future assessments of the impact of BMPs on nutrients in groundwaters. The findings reveal that BMPs should be better linked to groundwater nutrient monitoring programs in order to more quickly identify BMP deficiencies, and to dynamically adjust nutrient loadings to help achieve water quality objectives.

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)0.000
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 score0.253
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.215
Teacher spread0.208 · 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

Citations106
Published2006
Admission routes1
Has abstractyes

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