MétaCan
Menu
Back to cohort

Palestinian Water II: Climate Change and Land Use

2010· article· en· W1999518580 on OpenAlexaff
Marwan A. Hassan, Khaled Shahin, Brian Klinkenberg, Graham McIntyre, Mousa Diabat, Abed Al‐Rahman Tamimi, Ronit Nativ

Bibliographic record

VenueGeography Compass · 2010
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsUrbanizationClimate changeWater resourcesEnvironmental sciencePrecipitationPopulationNatural resource economicsWater resource managementPopulation growthWater qualityEnvironmental planningGeographyEconomicsEcologyMeteorologyEconomic growth

Abstract

fetched live from OpenAlex

Abstract Future predictions regarding the effects of climate change on water resources are complex and therefore inherently uncertain. However, the conclusions presented in most studies on the subject indicate that current water‐poor regions such as the Middle East will experience even greater water stress with climate change. In this article we find that Palestinian and Israeli water managers must plan for future water crises that will likely result from the combined effects of climate change and increasing urbanization that results from an exponential population growth. Climate change will likely increase water stress in the region through the processes of increasing temperature, decreasing and erratic precipitation and reduced overall aquifer replenishment. Urbanization will further strain freshwater supplies by negatively impacting the quality and quantity of available freshwater in an increasingly populated urban environment. In spite of any inherent uncertainty, water managers in the region need to consider the long‐term effects of both urbanization and climate change in any future water management scheme.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.015
GPT teacher head0.180
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueGeography CompassSame topicWater resources management and optimizationFrench-language works237,207