Enabling efficiency-driven and low-impact water management from robust decision making: A risk- and robustness-based multi-objective decision support model
Bibliographic record
Abstract
Increasing water-use efficiency across all sectors has been set as a key target for sustainable water management within the framework of the United Nations Sustainable Development Goals . Agriculture stands out as a key sector where opportunities for sustainable transformation arise, due to its dominant position in water use. The aim of this study is to propose a risk- and robustness-based multi-objective decision support model (RARB-MODSM) tool to help explore water-efficient agricultural development schemes and to enable inclusion of their associated environmental impacts into a stringent assessment of the risks. This tool allows for integration of economic growth and water use objectives with stringent pollution control requirements to help gain insights into the trade-offs between resource efficiency and environmental impact. This paper provides a case study of the application of this decision support tool in a prefectural-level city in China as an example of demonstrating how the tool could help reduce the water usage from the agricultural sector while enabling a higher economic productivity. The results show that expanding agricultural production in two regions of the city may help promote efficiency-oriented water management and improve the entire agricultural system's economic productivity. The results also indicate that this agricultural system is more sensitive to the control over phosphorus discharge and imposing more stringent total phosphorus (TP) control requirements within local agricultural system may help achieve better results on the system-wide pollution control. Overall, this tool demonstrates the applicability of using the systems analysis approach to help navigate the agricultural transition toward a water-efficient and low-impact growth path.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".