Sources and fate of nitrate in groundwater at agricultural operations overlying glacial sediments
Bibliographic record
Abstract
Abstract. Leaching of nitrate (NO3−) from animal waste or fertilizers at agricultural operations can result in NO3− contamination of groundwater, lakes, and streams. Understanding the sources and fate of nitrate in groundwater systems in glacial sediments, which underlie many agricultural operations, is critical for managing impacts of human food production on the environment. Elevated NO3− concentrations in groundwater can be naturally attenuated through mixing or denitrification. Here we use snapshots of the stable isotope values of NO3− to quantify denitrification in groundwater at two confined feeding operations overlying glacial sediments in Alberta, Canada. Uncertainty in δ15NNO3 and δ18ONO3 values of the NO3− source and denitrification enrichment factors are accounted for using a Monte Carlo approach. When denitrification could be quantified, we reconstructed the initial NO3-N concentration and NO3-N / Cl− ratio at the point of entry to the groundwater system. The addition of NO3− to the local groundwater system from temporary manure piles and pens equalled or exceeded NO3− additions due to leaching from earthen manure storages at these sites. Nitrate attenuation at both sites is attributed to a spatially variable combination of mixing and denitrification, but is dominated by denitrification. On-site denitrification reduced agriculturally derived NO3− concentrations by at least half and, in some wells, completely. These results indicate that infiltration to groundwater systems in glacial sediments where NO3− can be naturally attenuated is likely preferable to off farm export via runoff or drainage networks. The application of isotopes of nitrate to constrain a mixing model based on concentrations of Cl− and NO3−, which can be routinely monitored in groundwater, provides a relatively simple method to assess the sources and fate of agriculturally derived NO3− in these settings.
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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 | 0.001 | 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".