Integration of water replenishment and pollutant reduction to achieve ecological restoration goals based on sustainability of the lacustrine wetlands
Bibliographic record
Abstract
Successful wetland restoration is frequently constrained by the absence of persistent attention to the whole remedial process. The paper put forward a holistic method to restore the lacustrine wetland ecosystem. Yilong Lake wetland, one of the nine largest lakes of the Yun-Gui Plateau in China, was used as a case study. A modified (Pressure-State-Response) PSR model was presented to establish a comprehensive indicator system and to explain the ecological sustainability. Ecosystem sustainability and water quality were set as the general restoration target and the constraint restoration target, respectively, that makes the restoration goals not only contains the whole ecosystem but also the key individual parts. Two restoration goals (high and low) were set based on the cluster analysis of the historical data from 1952 to 2006. Different restoration levels give the decision makers and managers flexible options to restore the ecosystem based on the actual demand and practical capacity. Three restoration scenarios about the water replenishment and pollutant reduction were set to improve the ecological condition. The results showed that the integrated restoration measures according to water quantity and water quality can feasibly achieve the prescribed restoration levels. The paper gives the decision makers a holistic method to solve problems in lacustrine wetland restoration process.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".