Considerations for the mitigation of nitrate contamination: stable isotopes and insights into the importance of soil processes
Bibliographic record
Abstract
Nutrient management is widely promoted to minimize the impact of intensive fertilizer use on groundwater quality, however watershed-scale stable isotope studies in eastern North America suggest nitrogen transport to groundwater is dominated by non-growing season fluxes derived principally from the mineralization and nitrification of soil organic matter. In the current field scale study, delta15N ratios of nitrate in tile drain effluents from experimental potato plots treated with 300 kg/ha ammonia nitrate and those with no fertilizer both average +4.7 per thousand, close to the +4.0 per thousand ratios observed in soils of the same plots, and distinct from values near 0 per thousand for inorganic fertilizer. A source apportionment model using delta15N and delta18O in nitrate suggests that even with heavy fertilizer application, less than 10% of non-growing season N flux is derived from direct leaching of fertilizer, the remainder representing N from various sources, including residual fertilizer that has been assimilated into the broader soil organic matter pool and subsequently released via mineralization and nitrification. Factors controlling these losses could be as closely related to cropping practices as initial N application rates, providing potential opportunities for more efficiently utilizing N available in the soil profile and reducing initial N application rates.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".