Coupling nitrogen removal and watershed management to improve global lake water quality
Bibliographic record
Abstract
Abstract Lakes play a vital role in nitrogen (N) removal and water quality improvement, yet their efficiency varies due to differing watershed N input and lake characteristics, complicating management efforts. Here we established the N budget for 5768 global lakes using a remote sensing model. We found that watershed N input reduction and lake water quality improvement are nonlinearly related and depends on lake N removal efficiency. A 30% reduction in N loading in watersheds with high N removal efficiencies can improve cumulative water quality by over 70%. Stricter reduction could accelerate achieving water quality goal (≤1 mg N L–1), shortening the time by up to 30 years for most lakes. However, heavily polluted lakes with low N removal efficiencies (50 of 534 lakes with >1 mg N L–1) may not achieve the UN’s clean water SDG by 2030, even with a 100% N input reduction. Our research highlights the need for targeted N management strategies to improve global lake water quality.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".