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<sup>-1</sup> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".