Designed Overflow For Flood Recession Agriculture Against Flow Control For Hydroelectric Production In An African Context
Bibliographic record
Abstract
Manantali is a reservoir located in Mali, along the Senegal River, controlling about 50% of the total water flow of this river. Manantali is an annual reservoir, used to regulate the flow in the face of the extremely variable seasonal climate of the region. Manantali is mainly managed for agricultural and hydropower purposes, and its benefits are shared among Mali, Mauritania, and Senegal, the countries that participate to the project. The reservoir has been operative for about 10 years now, exceeding the planned capacity of hydroelectric production and irrigable land surface. The economic benefits due to the reservoir come at a price. Before the dam’s construction, the annual flood was the basis of flood recession agriculture, practiced traditionally by the local populations. Flood recession farming is based on natural irrigation and fertilization of the flood plain. Under the present management, flood recession agriculture is secondary to hydroelectric production and irrigation. These two objectives, in fact, require a more regular flow; therefore flow peaks are dumped in the reservoir. Annual floods are still produced, but they have been largely reduced. Moreover, the water authority are evaluating the construction of 6 more reservoirs, which will enhance even further the controllability of the river flow. In this study we explore the possible optimal compromises among the conflicting objectives of flood recession agriculture against hydroelectric production and irrigation. Moreover, we examine how a better use of hydrological information can improve the present reservoir management, in order to find a win-win solution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".