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Record W2981345092 · doi:10.1080/1573062x.2019.1669190

Towards a water secure future: reflections on Cape Town’s Day Zero crisis

2019· article· en· W2981345092 on OpenAlexaff
Lina Taing, Chun‐che Chang, SM Pan, Neil Armitage

Bibliographic record

VenueUrban Water Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsResilience (materials science)Water supplySustainabilityBusinessGovernment (linguistics)Per capitaNatural resource economicsWater scarcityPopulationWork (physics)Non-revenue waterEnvironmental planningWater usePrivate sectorEcological footprintWater resourcesWater resource managementEnvironmental resource managementWater conservationEnvironmental scienceEconomic growthEnvironmental engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

Capetonians have relied on dams to meet their needs for over a century. Extremely limited rainfall between 2015–2018, however, forced the City to impose a 50-litres per capita per day water restriction on its four million residents to avoid supply cut-offs. Cape Town’s water crisis highlights the importance of moving away from past infrastructural practices. South Africa needs a new water paradigm that embeds water sustainability and resilience in day-to-day practices that, inter alia, protects the natural water systems and ensures a sustainable water supply through reducing the environmental footprint of a growing population and developing alternative supply systems to dam infrastructure. To accomplish this, government, the private sector and consumers need to work together to develop and implement a water sensitive approach that will transform water planning, supply and demand at scale.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.141
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0120.011
Scholarly communication0.0100.010
Open science0.0020.007
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0160.001

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations40
Published2019
Admission routes1
Has abstractyes

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