Global patterns of nitrate isotope composition in rivers and adjacent aquifers reveal reactive nitrogen cascading
Bibliographic record
Abstract
Abstract Remediation of nitrate pollution of Earth’s rivers and aquifers is hampered by cumulative biogeochemical processes and nitrogen sources. Isotopes (δ15N, δ18O) help unravel spatiotemporal nitrogen(N)-cycling of aquatic nitrate (NO3−). We synthesized nitrate isotope data (n = ~5200) for global rivers and shallow aquifers for common patterns and processes. Rivers had lower median NO3− (0.3 ± 0.2 mg L−1, n = 2902) compared to aquifers (5.5 ± 5.1 mg L−1, n = 2291) and slightly lower δ15N values (+7.1 ± 3.8‰, n = 2902 vs +7.7 ± 4.5‰, n = 2291), but were indistinguishable in δ18O (+2.3 ± 6.2‰, n = 2790 vs +2.3 ± 5.4‰, n = 2235). The isotope composition of NO3− was correlated with water temperature revealing enhanced N-cascading in warmer climates. Seasonal analyses revealed higher δ15N and δ18O values in wintertime, suggesting waste-related N-source signals are better preserved in the cold seasons. Isotopic assays of nitrate biogeochemical transformations are key to understanding nitrate pollution and to inform beneficial agricultural and land management strategies.
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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".