Decadal Geochemical and Isotopic Trends for Nitrate in a Transboundary Aquifer and Implications for Agricultural Beneficial Management Practices
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".