An Eco-Compensation Strategy in the Water Source Area: A Case for Southern Shaanxi in China
Bibliographic record
Abstract
The South-to-North Water Transfer Project (SNWTP) in China which is the world’s largest water transfer project completed in 2014 is promoted as a strategy to mitigate water stresses in the northern China by the Chinese government and engineers. However, the ecological compensation of SNWTP in China has been slow and less for the people living in the water source area which was lost their opportunities to earn money; some had even lost their farmland. A key issue for SNWTP is to establish an eco-compensation system, define the compensation scheme, and make an effective economic compensation to the people living in the water source area. In this paper, we take the water source area of the Middle-Route project of SNWTP located in Southern Shaanxi including three cities called Hanzhong, Ankang and Shangluo as the research regions. Six factors are taken into consideration: (1) ecological losses, (2) economic losses and (3) ecological bonus in our eco-compensation strategy, as well as join two dynamic factors accounting in the calculation of ecological damage, (4) water quantity and (5) water quality. Besides, the total amount of compensation is changing over time. (6) Time scale factor is also used to simulate. In the article, we set three-time periods to calculate the different amount of compensation for the water source area. Finally, the Southern Shaanxi, supplied 70% water to Danjiangkou Reservoir, were analyzed by this model. We make full use of the reasonable eco-compensation strategy and try to solve actual problems of water source area and even provide a basis conception for the watershed protection and management.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".