How successful are the restoration efforts of China's lakes and reservoirs?
Bibliographic record
Abstract
China has made considerable efforts to mitigate the pollution of lakes over the past decade, but the success rate of these restoration actions at a national scale remains unclear. The present study compiled a 13-year (2005-2017) comprehensive dataset consisting of 24,319 records from China's 142 lakes and reservoirs. We developed a novel Water Quality Index (WQI-DET), customized to China's water quality classification scheme, to investigate the spatio-temporal pollution patterns. The likelihood of regime shifts during our study period is examined with a sequential algorithm. Our analysis suggests that China's lake water quality has improved and is also characterized by two WQI-DET abrupt shifts in 2007 and 2010. However, we also found that the eutrophication problems have not been eradicated and heavy metal (HM) pollution displayed an increasing trend. Our study suggests that the control of Cr, Cd and As should receive particular attention in an effort to alleviate the severity of HM pollution. Priority strategies to control HM pollution include the reduction of the contribution from mining activities and implementation of soil remediation in highly polluted areas. The mitigation efforts of lake eutrophication are more complicated due to the increasing importance of internal nutrient loading that can profoundly modulate the magnitude and timing of system response to external nutrient loading reduction strategies. We also contend that the development of a rigorous framework to quantify the socioeconomic benefits from well-functioning lake and reservoir ecosystems is critically important to gain leeway and keep the investments to the environment going, especially if the water quality improvements in many Chinese lakes and reservoirs are not realized in a timely manner.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".