MétaCan
Menu
Back to cohort
Record W2805030611 · doi:10.5539/jsd.v11n3p23

Water Security and Local People Sensitivity to Climate Variability and Change Among Coastal Communities in Zanzibar

2018· article· en· W2805030611 on OpenAlexvenueno aff
Makame Omar Makame, Richard Y. M. Kangalawe

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsRainwater harvestingWater supplyEnvironmental scienceWater qualityWater resource managementWater securityVulnerability (computing)AgricultureWater resourcesClimate changeSurface runoffWater scarcityIrrigationGeographyOceanographyEcologyEnvironmental engineering

Abstract

fetched live from OpenAlex

This paper presents one part of a larger, multidimensional study on the vulnerability of Zanzibar coastal communities to climate change and other stressors, focusing on water insecurity in two sites on the Zanzibar coast. Water security is composed of three components, namely water availability, water accessibility and the quality of available water. Findings from the study showed that water from wells is the major source of household water since tap water supply is often erratic. Farming systems are completely rain-fed, while some livestock owners use coral caves as the main source of water for their animals. Water quality is a major challenge along the coast as the majority of wells and coral caves in the study sites contain hard water with varying levels of salinity. A diversity of physical and social factors such as variability in water supply infrastructure, settlement structure, poverty, geology and geohydrology, variability in supply and poor water resource management adversely interact with local climate phenomena such as sea level rise, salt water incursion and drought to intensify water insecurity along the Zanzibar coast. Among the policy options to address these challenges would be to promote rainwater harvesting and increased utilisation of underground water for irrigation in the dry areas in order to increase agricultural production and reduce poverty.

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)0.006
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 score0.298
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.279
Teacher spread0.240 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicClimate Change, Adaptation, MigrationFrench-language works237,207