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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
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 score0.650
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
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