Follow-Up of Social Impacts in Senegal Watershed After Manantali and Diama Dams Building
Bibliographic record
Abstract
ISEE-624 Abstract: The Senegal river was, together with the Niger river, the center of most West African medieval kingdoms. In this area, we have a millenary farming civilization based on the harmonious exploitation of the rise and drop in water level by farmers, cattle farmers, and fishermen. Since French colonization, a lot of management has been done in the Senegal watershed. But in the 1970s, Mali, Mauritania, and Senegal states combined their efforts in a regional organization named OMVS to face drought and climate change in the Sahel. Senegal river, which is a transboundary resource known in this decade had the lowest hydraulicity in the 19th century. The OMVS Program proposed to build 2 large dams, one (Manantali Dam) in high Senegal basin and another (Diama Dam) in the delta to stop penetration of sea water. Objectives of watershed management are, among others, to produce hydroelectricity and to develop irrigate farming in Senegal river. After achieving this program, social impacts remain major after mitigation measures application. Local populations do not have tradition of irrigation and all their farming civilizations are based on exploitation of rich lands after drop in water level. Otherwise, we have the propagation of bilharzias and malaria with prevalence rates never reached before the building of the dams, particularly in Richard-Toll health district where we have the largest irragated farming. This paper presents current stakes of Senegal watershed management (human health, traditional farming, etc.), the risk perception by local populations, and health programs driven by OMVS; it also proposes some tools to improve mitigation measures and explains how to achieve local development among watershed integrated management.
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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 | 0.002 | 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 | 0.000 |
| 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 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".