Assessing water supply capacity in a complex river basin under climate change using the logistic eco-engineering decision scaling framework
Bibliographic record
Abstract
Abstract. Climate change is a global stressor that can undermine water management policies developed under the assumption of stationary climate, necessitating robust solutions to reducing the risk of system failures for uncertain future climates. While the response-surface-based assessments have provided convenience to explore responsive behaviours of expected system performance to climatic stresses, they were unable to predict the risk of system failures from individual climate projections. In this study, we proposed to use the logistic regressions for evaluating the probability of non-successive outcomes against pre-defined thresholds directly from climate projections, which may be more informative for decision making processes than the expected performances. As a case study, water supply and ecological reliabilities within a large river basin were assessed by combining the eco-engineering decision scaling framework and the logistic regressions. The impact assessment for the South Korean river basin showed that optimal water supply performance at the sub-basins were expected to be satisfactory for the upcoming 20 years of 2020–2039, while the human-demand-only operations could lower the ecological reliabilities. When considering ecological demands in water operations to reduce the ecological vulnerabilities, the stakeholders should accept increasing risks of unsatisfactory supply at the sub-basins with low demands. This study highlights that binary conversions of the performance metrics from the stress tests allow users to measure the risks of system failures varying across sub-components and standpoints with minimal computational costs.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".