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Record W2054210482 · doi:10.1080/07900627.2013.837367

Rainwater and greywater harvesting for urban food security in La Soukra, Tunisia

2013· article· en· W2054210482 on OpenAlexaff
Mark Redwood, Moez Bouraoui, Boubaker Houmane

Bibliographic record

VenueInternational Journal of Water Resources Development · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsRainwater harvestingGreywaterBusinessAgricultureFood securityReuseUrban agricultureSustainabilityIrrigationAgricultural productivityWater resource managementAgricultural economicsEnvironmental scienceNatural resource economicsEnvironmental engineeringEconomicsGeographyEngineeringWastewater

Abstract

fetched live from OpenAlex

This paper presents the findings of an integrated household water treatment and reuse system for agriculture in La Soukra, Tunisia. The researchers found that the system has an internal rate of return of 17% and a net present value range from USD 26,000 (at a 5% discount rate) to USD 11,000 (for a 10% discount rate). Benefits included more water for irrigation, reduced costs to service providers, increased agricultural production from greenhouses and expanded agricultural options. These results suggest that investments in rainwater harvesting and greywater treatment at the farm level can increase the financial feasibility of peri-urban farms, which are often faced with pressure from urban growth. The systems can also help build household resilience to broader environmental change by lowering the exposure of farmers to burdens associated with infrequent access to water and poor-quality soil.

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)0.001
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 score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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