Risk analysis of rich–poor rainfall encounter in inter-basin water transfer projects based on Bayesian networks
Bibliographic record
Abstract
An inter-basin water transfer project is one of the effective ways to resolve the problem of an uneven distribution of water resources. Temporal and spatial variations in rainfall in different basins greatly affect water supply and demand in inter-basin water transfer projects, leading to risks to the operation of the water transfer projects. This paper applies a Bayesian network model to analyze this risk and studies the rich–poor rainfall encounter risk between a water source area and water receiving areas in the middle route of the South-to-North Water Transfer Project in China. Real time scenario simulations with the input of new observations were also studied. The results show that the rich–poor rainfall encounter risk is high for the Tangbai River receiving area in the fourth quarter, for the Huai River and South of Hai River receiving area in the second quarter, and for the North of the Hai River receiving area in the fourth and first quarters. The scenario simulations reflect risk change in the operation of water transfer projects, providing scientific decision support for the management of the water resource distribution in the inter-basin water transfer projects.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".