Restoring small water bodies to improve lake and river water quality in China
Bibliographic record
Abstract
Abstract Climate change, population growth, and agricultural intensification are increasing nitrogen (N) inputs, while driving the loss of inland water bodies that filter excess N. However, the interplay between N inputs and water body dynamics, and its implications for water quality remain poorly understood. Analyzing data from 1995 to 2015 across China, here, we find a 71% reduction in the area of small (<10 4.5 m 2 ) water bodies (SWB), primarily in high-N-input agricultural regions. Preferential loss of SWBs, the most efficient nutrient filters, places 42% of China at high water quality risk due to increasing N inputs and declining SWB density. Currently, N removal by water bodies is 986 kilotonnes year −1 , but restoring 2.3 million hectares of SWB could increase removal by 21%, compared to just 5% for equivalent restoration of large water bodies. Targeted SWB restoration is crucial for improving water quality and mitigating N pollution in China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".