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Record W4405091985 · doi:10.1029/2023ef003979

Future Pathways of Water, Energy, and Food in the Eastern Nile Basin

2024· article· en· W4405091985 on OpenAlexafffund
Ahmed Abdelkader, Amin Elshorbagy, Mohamed Elshamy, H. S. Wheater

Bibliographic record

VenueEarth s Future · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural basinEnvironmental scienceGeographyWater resource managementGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract The Eastern Nile Basin (ENB) countries of Egypt, Sudan, South Sudan, and Ethiopia are subject to pronounced water, energy, and food (WEF) insecurity. There is a need to manage the WEF nexus to meet rapidly increasing demands, but this is extremely challenging due to resource scarcity and climate change. If countries that rely on shared transboundary water resources have contradictory WEF plans, that could diminish the expected outcomes, both nationally and regionally. Egypt as the most downstream Nile country is concerned about ongoing and future developments upstream, which could exacerbate Egypt's water scarcity and affect its ability to meet its WEF objectives. In this context, we introduce a multi‐model WEF framework that simulates the ENB water resources, food production, and hydropower generation systems. The models were calibrated and validated for the period 1983–2016, then utilized to project a wide range of future development plans, up to 2050, using four performance measures to evaluate the WEF nexus. A thematic pathway for regional development that shows high potential for mutual benefits is identified. However, the WEF planning outcomes for the region are sensitive to climate change, but, if social drivers could be managed (e.g., by lowering population growth rates) despite the difficulties involved, climate change impacts on WEF security could be less severe.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.178
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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