Quantifying the Sustainability of Water Availability for the Water‐Food‐Energy‐Ecosystem Nexus in the Niger River Basin
Bibliographic record
Abstract
Water, food, energy, and the ecosystems they depend on interact with each other in highly complex and interlinked ways. These interdependencies can be traced particularly well in the context of a river basin, which is delineated by hydrological boundaries. The interactions are shaped by humans interacting with nature, and as such, a river basin can be characterized as a complex, coupled socioecological system. The Niger River Basin in West Africa is such a system, where water infrastructure development to meet growing water, food, and energy demands may threaten a productive and vulnerable basin ecosystem. These dynamic interactions remain poorly understood. Trade-off analyses between different sectors and at different spatial scales are needed to support solution-oriented policy analysis, particularly in transboundary basins. This study assesses the impact of climate and human/anthropogenic changes on the water, energy, food, and ecosystem sectors and characterizes the resulting trade-offs through a set of generic metrics related to the sustainability of water availability. Results suggest that dam development can mitigate negative impacts from climate change on hydropower generation and also on ecosystem health to some extent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".