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Record W2760137809 · doi:10.2495/sdp-v13-n3-394-405

Sustainability of basin level development under a changing climate

2018· article· en· W2760137809 on OpenAlexvenueno aff
Ibrahim Alameddine, Abbas Fayyad, Majdi Abou Najm, M. El‐Fadel

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2018
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersUnited States Agency for International Development
KeywordsStructural basinSustainabilityEnvironmental scienceClimate changeWater resourcesDrainage basinWater resource managementSustainable developmentMediterranean BasinEnvironmental resource managementMediterranean climateHydrology (agriculture)GeographyEngineeringGeology

Abstract

fetched live from OpenAlex

The potential impacts of projected future climate change scenarios on the hydrologic response of a water-stressed Mediterranean river basin (Upper Litani River Basin in Lebanon) are quantified and assessed using the Water Evaluation and Planning (WEAP) model. Projected basin-level changes in water availability are then compared to multi-sector demands estimated under six basin-level development scenarios. The sustainability under these scenarios and the resilience of the system in the face of the projected climatic changes are then assessed in terms of a water resources index, demand reliability, demand satisfaction index, demand reliability index and the average duration of failure. The results reveal that the basin is expected to experience significant alteration in its hydrologic cycle and that current plans envisioning an increase in irrigated areas within the basin, is non-sustainable and will lead to a highly water stressed system. A conservative basin-level plan that integrates both supply-and demand-side measures is proposed in an effort to achieve a more sustainable system.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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 designQualitative
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

Citations13
Published2018
Admission routes1
Has abstractyes

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