Efficient and Economical Allocation of Irrigation Water under a Changing Environment: a Stochastic Multi‐Objective Nonlinear Programming Model*
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Water scarcity causes conflicts between natural resources and socio‐economic development which reinforces the need for optimal allocation of irrigation water resources. Irrigation water resource allocation is a complex problem due to various uncertainties in natural conditions. In this study, a stochastic multi‐objective nonlinear programming model is developed for irrigation water allocation under uncertainty. The model is capable of balancing the conflicting objectives of maximizing both net economic benefit (NEB) and irrigation water use efficiency (IWUE). Moreover, it can reflect the random nature of water availability, and provide alternative water allocation schemes in response to climate change. The applicability of the developed model is demonstrated by a case study in north‐west China. Trade‐offs between NEB and IWUE are presented. Irrigation water allocation schemes to cope with changing environments, including climate change and varying water availability, are also proposed. The results demonstrate that the developed model can generate solutions that save irrigation water while ensuring NEB. This model is a useful tool to support the formulation of optimized water resources management policies in a changing environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it