Optimization of Water-Food Nexus System under Dual Uncertainties
Bibliographic record
Abstract
Abstract In this study, a fuzzy chance-constrained programming (FCCP) method is developed to synergetic plan water-food nexus (WFN) system under dual uncertainties. The developed method can tackle uncertainties expressed as probabilistic distribution and flexible parameter. Then, a FCCP-WFN model is formulated for the city of Jinan (China), in which 72 scenarios are designed with the consideration of different food demand levels, constraint-violation risk levels, and satisfactory degrees. Results indicate that (i) surface water would be the main water source for Jinan (accounting for 62.7% of water supply), and agriculture would be the largest water consumer (accounting for 55.5% of water allocation), therefore, rational management of surface water and reduction of agricultural water allocation are essential to alleviate the water shortage problem in Jinan; (ii) the annual arable land area is 628.4×103 ha to 683.4×103 ha, of which grain crops account for 62.1%, and the sufficient grain planting area can ensure food security in Jinan; (iii) uncertainties have significant influence on water allocation schemes, thus, managers should consider the impact of uncertainties during decision-making process, and make different management schemes according to different attitudes to system risks under undetermined conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".