Jonglei Canal Project Under Potential Developments in the Upper Nile States
Bibliographic record
Abstract
Nile basin countries are experiencing water scarcity due to rapid growth in population and climate change. This scarcity drew attention to the vast amount of water lost in the swamp areas of the Nile basin. Preventing this water loss is essential for reducing the food gap and promoting development in all Nile countries. Jonglei Canal is an important project that was proposed to reduce the vast water losses in the Sudd region in Southern Sudan. The Jonglei Canal project was launched and stopped in the 1980s due to civil war in Sudan. Recently Upper Nile riparian countries have published their plans for possible development projects which might significantly reduce flow to the Sudd region and hence reduce the potential water savings from Jonglei Canal. In addition, environmental concerns about the Jonglei Canal project have been raised by local tribes, that the project may reduce the size of swamps and adversely affect their grazing activities. This paper investigates the impact on the feasibility of the Jonglei Canal project of the proposed development projects in the upstream countries. The projected size of the swamp area is quantified under different scenarios of upstream development and Jonglei Canal operation. The Nile decision support tool (Nile DST model) and a HEC-RAS model were used for hydrologic and hydraulic simulations of the White Nile system. It was found that the ambitious expansion of irrigation projects may affect the benefits of the Jonglei Canal project.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".