Perspectives on small watershed management in China: the case of Biliu
Bibliographic record
Abstract
River basin management in China has focused on large rivers, such as the Yangtze and Yellow Rivers. However, there are also about 20,000 small river basins that deserve attention. The Biliu River basin in Liaoning Province, northeast China, which is 156 km in length, has many features common to small river basins, including divided jurisdiction among governments leading to vertical and horizontal fragmentation. In the Biliu basin, the key municipalities are Dalian with a population of almost 6 million people and Yingkou with about 2.28 million people. Each has different interests and needs, making coordination and collaboration difficult. The literature and practice suggest that effective integrated water resource management (IWRM) requires at least six features: (1) vision, policy and strategy; (2) a legislative or statutory basis to provide credibility and authority; (3) a river basin organization to coordinate development and management; (4) specification of functions and authority of the river basin organization; (5) public participation and community involvement; and (6) an IRWM strategy and plan. Water management in the Biliu River basin is assessed relative to these features of IWRM, with recommendations offered regarding changes to have water management in the Biliu River basin to reflect what is expected in an IWRM approach.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".