Replenishing the Indus Delta through multi-sector transformation
Bibliographic record
Abstract
The Indus River Basin (IRB) is a severely water-stressed and rapidly developing home to an estimated 250 million people in South Asia. An acute deficit of environmental flows (EFs) in the basin’s delta negatively impacts geomorphology and surrounding ecosystems. Here, a sub-national model of the IRB’s integrated water–energy–land systems is applied to quantify multi-sector transformations and system costs for enhancing EFs to the Indus Delta. The results show that increasing the average outflows from the basin relative to historical policy levels by 2.5 and 5 times would increase sectoral costs for upstream water users between 17–32 and 68–72% for low and high ecological potential targets. The enhanced EFs result in more energy for pumping and treating water upstream from the delta and a net increase in irrigation and energy investments. The EF policy costs are minimized by 7–14% through cooperation across countries and 6–9% through the coordinated implementation of water efficiency measures in the irrigation, conveyance, power plant cooling, and water treatment sectors. The results underscore the crucial role of a multi-sector, multi-scale collaboration in achieving EF targets in water-stressed river basins for ecosystem adaptation to climate vulnerability, restoration of the delta, and socio-economic benefits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".