Water Compensation and Its Implication of the Three Gorges Reservoir for the River-Lake System in the Middle Yangtze River, China
Bibliographic record
Abstract
Dam construction is an important means to improve water use efficiency and the aquatic environment. However, the flow regulation of the Three Gorges Reservoir (TGR) in the middle Yangtze River has attracted much attention because the severe drought occurred in the river-lake system downstream of the TGR. In this paper, the Dongting Lake was selected as a case study in order to detect the possible relationship between the flow regulation of the TGR and the extreme drought in the river-lake system based on a coupled hydrodynamic model. The results not only confirmed the significant role of the TGR to relieve drought in the river-lake system, but also indicated that the outflow of the TGR and the hydraulic gradient between the Zhicheng to Chenglingji stations were the crucial factors to affect the water exchange between the rivers and the Dongting Lake. The adjustment of hydraulic gradient within a proper range during the water compensation of the TGR will be an effective measure to improve the water exchange and water environment in the river-lake system. These findings present the quantitative influence of these important factors on the water exchange between rivers and lakes and provide a scientific reference for environmental and ecological management of other river-lake systems.
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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 | 0.001 |
| 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) | 0.002 | 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".