Non-deterministic multi-level model for planning water-ecology nexus system under climate change
Bibliographic record
Abstract
Water scarcity and ecological degradation impede sustainable development in Central Asia, which urgently calls for synergistic planning of water-ecology (WE) nexus system. However, existing management models may have large uncertainties, restricting their effectiveness. Here, we develop a copula-based flexible fuzzy multi-level programming (CFMP) method, which tackles uncertainties, such as water demands, and balances trade-offs among competing managers in top-down decision-making processes. Next, we formulate a CFMP-WE model for Central Asia (2021–2050), considering objectives of economic development, food security, and ecological restoration, and design 243 planning scenarios. We found that ecological water allocation would account for 5.9%–12.2% to support sustainable development; however, policymakers need to reduce agricultural water allocation, forgoing 7.8%–20.1% of the system benefit (i.e., economic benefit for WE nexus system). Agricultural water use would still be the largest (with 25.6%–29.4% for cereal crops to ensure food security), but its share would decline to conserve water for users like industry.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".