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Record W7116151188 · doi:10.1051/e3sconf/202568000100

Renewable energies powering seawater desalination in Morocco

2025· article· fr· W7116151188 on OpenAlexaff

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languagefr
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsRenewable energyDesalinationGeothermal desalinationGreenhouse gasWater-energy nexusFossil fuelSustainabilityWater desalinationSustainable development

Abstract

fetched live from OpenAlex

Several reasons including fast-paced development, global warming, unsustainable water use, and many others have prompted many areas around the world to experience water shortages. But with salt water accounting for approximately 97.5% of total water on Earth and its availability in almost every country, desalination has emerged as the go-to solution. This comes with some associated issues, namely, significant energy requirements. These are largely satisfied by fossil fuels, which are becoming increasingly scarce and contribute to greenhouse gas (GHG) emissions and, therefore, global warming. Thanks to renewables, desalination plants could be able to sustainably overcome such shortages, particularly in Morocco. This is attributable to the country’s favorable meteorological conditions for solar and wind plants, and to the large amount of land available for hydroelectric plants. This paper discusses the main benefits associated with the use of renewables in seawater desalination plants. Notably, the contribution to increase the share of renewables in the energy mix and to reduce GHG emissions. Moreover, it reviews certain obstacles encountered, including costs and grid stability issues, which could be complex and costly to overcome. The paper also highlights the necessity of adopting AI, IoT, and waste management technologies to further optimize processes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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.

Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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