Assessing and forecasting water security in transboundary river basins via inter- and intra- subsystem dynamics
Bibliographic record
Abstract
Mekong River basin. This paper attempts to propose a new framework for watershed water security assessment. The framework aims to construct a Synthetic Water Security (SWS) evaluation index system based on the Driver-Pressure-State-Impact-Response (DPSIR) model from four dimensions of vulnerability, sensitivity, development and sustainability, and to analyse the level of SWS in transboundary river basins by using a Comprehensive Co-Evolution Model (CCEM) that takes into account both absolute and relative adaptability. The CCEM model was used to evaluate the SWS in the Mekong River basin (MRB), and the Long Short-Term Memory (LSTM) is used to predict the SWS development trend of the MRB in the future. The results show that the weight analysis of each subsystem is development > vulnerability > sustainability > sensitivity. Specifically, the vulnerability subsystem displays a fluctuating downward trend, the sensitivity subsystem shows a trend that decreases initially and then increases, while the development and sustainability subsystems exhibit a consistent upward trend. From 1995–2020, the SWS of MRB rose from a low level to a medium level. The SWS of all the riparian countries showed a positive trajectory. Between 2025 and 2030, China’s SWS will be anticipated to remain at a high level, while other countries are expected to stay at a medium level. • The concept of synthetic water security is proposed from a system perspective. • Use the Comprehensive Co-Evolution Model to assess synthetic water security. • Use the Long Short-Term Memory to predict the synthetic water security levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".