The challenge of integrating local communities to evaluate the WEFE Nexus: the example of the Senegal River
Bibliographic record
Abstract
<p>Managing the WEFE nexus is a prerequisite towards the sustainable management of water and connected resources at the river basin scale. The challenge is even more important in transboundary river basins such as the Senegal River where the four riparian countries (Guinea, Mali, Mauritania, Senegal) share a common vision: to transform the river into a food-energy-transportation hub in West Africa. This vision requires the construction of hydroelectric power plants, modern irrigation systems and river transport infrastructure, and often requires constant river flows. Several dams already exist and several more are planned in the coming decades. These developments directly threaten the livelihoods of riverine communities that depend on the river banks, mainly for flood-recession agriculture and fishing.</p><p>Evaluating the WEFE nexus and its objectives usually involves indicators describing how well the sectoral objectives are met in terms of food production, energy production or percentage of the population with access to safe drinking water and sanitation services. These indicators are often aggregated at the river basin level, which masks local realities: the water needs, water management practices water management practices and inequalities in access to water at communities or households’ level.</p><p>The objective of this communication is to present a portfolio of regional and community-level indicators to analyze the WEFE nexus across different spatial and temporal scales. The construction of this portfolio is a two-stage process. Firstly, the definition of relevant indicators is carried out on the basis of consultation with a variety of participants involved in water management in the Senegal River (researchers and experts, institutional stakeholders, civil society actors, local communities). Secondly, local data describing the state of the local indicators of the nexus at the local level are collected from field surveys. We will also show how these indicators can enrich the analysis of basin-wide hydroeconomic models by assessing multi-scale trade-offs, as well as the distributional impacts of nexus interventions. This work is part of the H2020 GoNexus project, which aims at improving the governance of the WEFE nexus in transboundry river basins.</p>
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 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 teacher head, 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".