Sustainable Water‐Resources Allocation Through a Trading‐Oriented Mechanism Under Uncertainty in an Arid Region
Bibliographic record
Abstract
In this study, a dual‐interval two‐stage stochastic programming (DTSP) approach is developed for planning water resources through a market‐trading‐oriented mechanism under uncertainty. The DTSP method is used for policy analysis of water trading in the Kaidu‐kongque River Basin with the efficient and sustainable manners to relieve the pressures of economic development and population growth in northwestern China. The obtained results reveals that a market‐trading‐oriented mechanism under the adjustment/regulation by water managers would be a sustainable/effective manner to allocate water resources against the market failure. Meanwhile, the trade‐off between governmental regulation and market behavior has generated a more efficient trading mode with overall consideration of factors such as food safety, drinking safety, environmental protection, and regional development in a watershed system, which could bring about a number of beneficial impacts on regional water resources development. Moreover, the DTSP method is also appropriate for discriminating varied cases associated with various levels of economic influences since the penalties could be exercised with the recourses actions against any infeasibility.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".