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Record W4389348755 · doi:10.1080/07900627.2023.2273475

Small-scale desalination and atmospheric water provisioning systems in water-scarce vulnerable communities: status and perspectives

2023· article· en· W4389348755 on OpenAlexaff
Guilherme Baggio, Jan Adamowski, Victor James Hyde, Manzoor Qadir

Bibliographic record

VenueInternational Journal of Water Resources Development · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill UniversityMcMaster UniversityUnited Nations University Institute for Water, Environment, and HealthUniversity of Toronto
Fundersnot available
KeywordsProvisioningBusinessDesalinationWater scarcityScale (ratio)Citizen journalismEnvironmental planningEnvironmental resource managementWater resourcesWater supplyNatural resource economicsEnvironmental scienceEnvironmental economicsGeographyPolitical scienceEnvironmental engineeringEconomicsEcologyEngineering

Abstract

fetched live from OpenAlex

Small-scale desalination and atmospheric water provisioning systems can be vital for supplying drinking water in water-scarce areas. However, their potential to support vulnerable communities in such regions has not been fully assessed. Through an in-depth comprehensive review of 111 peer-reviewed publications from 1992 to 2023 and commercial technologies, this study shows significant knowledge gaps on implementing those systems in water-scarce vulnerable communities. To address knowledge gaps, research and implementation should align with local socio-economic, institutional and cultural contexts involving supportive policies, funding mechanisms, risk analysis, human resources, participatory approaches and consideration of community needs.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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