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Record W2893536930 · doi:10.1080/07900627.2018.1509787

Developing new urban water supplies: investigating motivations and barriers to groundwater use in Cape Town

2018· article· en· W2893536930 on OpenAlexafffund
Emma Luker, Leila M. Harris

Bibliographic record

VenueInternational Journal of Water Resources Development · 2018
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCapeDiversification (marketing strategy)GroundwaterWater supplyEnvironmental planningWater resource managementWater scarcityResource (disambiguation)Water resourcesGeographyBusinessEnvironmental scienceEnvironmental engineeringAgricultureEngineeringArchaeology

Abstract

fetched live from OpenAlex

Many cities are experiencing increasing water resource stress. In Cape Town, South Africa, surface water supplies are at a record low due to a multi-year drought crisis which began in 2015. This paper analyzes the range of motivations, possibilities and obstacles related to diversifying Cape Town’s water supply system through the upscaling of groundwater resources. Drawing on insights from local experts, it is maintained that uncertainty surrounding groundwater and drought-management practices present significant barriers to Cape Town’s ongoing water diversification efforts. This paper provides further insight and discussion for future water planning in Cape Town, as well as for other urban, water-scarce, regions.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.020
GPT teacher head0.219
Teacher spread0.199 · 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

Citations42
Published2018
Admission routes2
Has abstractyes

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