Assessing and forecasting water security in transboundary river basins via inter- and intra- subsystem dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| 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 it