Land use mediates riverine nitrogen export under the dominant influence of human activities
Bibliographic record
Abstract
Riverine nitrogen (N) export is a crucial process that links upstream and downstream ecosystems and coastal zones. However, the driving forces of riverine N export that is closely related to water N pollution are still not well understood. In this study, we used a mass balance approach to quantify the sources of N discharge and analyzed the effect of land use composition on riverine N export, taking Zhejiang Province, China as a case study. We found that the total reactive N discharge to rivers in Zhejiang increased from 0.22 to 0.26 Tg yr −1 from 2000 to 2015. At the watershed scale, our estimate of N export agrees well with the monitored riverine N concentration in the eight major watersheds in Zhejiang. Direct discharge of domestic wastewater and effluents from wastewater treatment plants are dominant sources of riverine N export, followed by agricultural non-point sources. Although riverine N export increases with the increasing proportion of urban and agricultural land uses, we did not find any relationship between land use change and changes in riverine N export. This suggests that the dominant factor affecting riverine N export should be human activities (e.g. wastewater discharge and fertilization level), while land use only mediates riverine N export.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".