Surface Water Expansion in the Huayang River Basin Following the Comprehensive Implementation of the River and Lake Chief System
Bibliographic record
Abstract
Over the past three decades, China’s rapid economic growth has significantly improved living standards but has also led to environmental challenges. The Chinese government launched the River Chief System and the Lake Chief System to promote the sustainable development. We selected the Huayang River basin to assess the impacts of the RLCS after the RLCS implementation. We analyzed Sentinel-1 GRD over 2015–2021 to extract annual water body information, and found: (1) The total surface water area in the Huayang River Basin expanded from 1005.66 km2 to 1241.09 km2. (2) Lake water surface areas expanded by 13.11 km2, river surface areas grew by 3.77 km2, and small and micro water bodies increased by 218.61 km2. (3) Notable increases in specific lakes were observed, with Longgan Lake growing by 4.09 km2, Po Lake by 2.20 km2, Huang Lake by 2.72 km2, Daguan Lake by 0.76 km2, and Dayuan Lake by 1.53 km2. (4) The shift to production-oriented fishing has restored aquatic ecosystems, transitioning aquaculture to coastal lowlands and converting farmland into shrimp-rice paddies and fish ponds. This study offers the first quantitative assessment of RLCS outcomes, providing policymakers in rapidly developing regions with insights and evidence-based strategies for sustainable water governance, applicable globally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".