Interval Multi-Random Factorial Programming for Coupled Farmland and Water Resources Management -- A Case Study of Songhua River Watershed, China
Bibliographic record
Abstract
The Songhua River Watershed (SHRW) in China has been challenged by water shortages, water pollution, water leakage, and soil erosion in recent years. In the next few decades, these problems will continue to exist and even worsen, threatening the quality of the regional ecological environment and socio-economic development. These issues must be alleviated through coupled farmland and water resources management (CFWRM) but are challenged by multiple system complexities. To fill this gap, this study developed an Interval Multi-Random Factorial Programming (IMRFP) to eliminate potential problems in SHRW and improve the reliability of the decision support process. A series of systematic CFWRM measures were applied to promote the harmonious SHRW ecological environ¬ment and social economy. For example, due to the significant contribution of agriculture to the regional economy, planting should always be a priority. As a major commercial crop, rice cultivation should be allocated the most irrigation water, followed by corn, potatoes, and soybeans. Therefore, after fully balancing the trade-off between the environment and the economy, policymakers should adopt the most reasonable proposals. Various support policies are needed to fully implement these measures in SHRW. For example, it is suggested to improve and update the construction of the water supply network in the SHRW area and appropriately change taxes and prices to follow the overall crop planting plan. The modeling solution shows that the IMRFP method can systematically optimize the allocation of water resources and farming patterns so that water shortage, water pollution, water leakage, and soil erosion in the SHRW can be alleviated.
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 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 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".