An Adaptive Water Resources Management Framework With Combined Policies to Confront Adverse Effects and Risks Due to Population-industry Transformation Into a Floodplain Area
Bibliographic record
Abstract
Abstract In this study, an adaptive water resource management framework with combined policies (AWFP) is developed for mitigating adverse effects on water resource in a floodplain area due to population-industry transformation in context of coordinative development of urban agglomeration. A location-entropy based PVRA model (LE-PCRA) and coupla-risk analysis (CRA) can be introduced to reflect the adverse effects of industrial information and driven population on water resources; meanwhile risks (including water shortage, soil loss and flood control) and corresponding correlations have been shown in the risk maps. Moreover, an adaptive scenario analysis based stochastic-fuzzy method (ASSF) can be embedded into an AWFP to deal with multiple uncertainties and their interactions due to subjective and artificial factors. The proposed AWFP is applied to a practical case study of Yongding river floodplain region for confronting adverse effects on water resources due to population-industry transformation in the context of coordinative development of Beijing-Tianjin-Hebei urban agglomeration, China. The results were obtained to reflect the negative effects of population-industry transformation and corresponding water allocation patterns in floodplain, which is effective to confront natural and artificial damages (such as water deficit, water and soil loss, and flood damage), risks and function degradation of floodplain contemporarily. Meanwhile, various policy scenarios (such as farmland returning to wetland, improvement of water resource utilization efficiency, water diversion and flood control) can be analyzed to support adjusting current population-economy strategies and water management patterns to accommodate source function of floodplain with a risk-averse and sustainable manner.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".