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Record W4413000886 · doi:10.1016/j.dwt.2025.101366

Selective electrodialysis technology: A viable solution to water quality challenges in Sri Lankan community-based organizations

2025· article· en· W4413000886 on OpenAlexfundno aff
Theekshana Malalagama, Binghui Tian, R.M.G. Rajapakse, Rehan Gunathilake, A.V.K. Mapa, Wmnd Ranasinghe, Min Yang

Bibliographic record

VenueDesalination and Water Treatment · 2025
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsnot available
FundersChina-Sri Lanka Joint Center for Research and EducationUniversity of Chinese Academy of SciencesChinese Academy of SciencesAlliance of International Science OrganizationsNational Key Research and Development Program of ChinaCanadian Anesthesiologists' Society
KeywordsElectrodialysisSri lankaQuality (philosophy)BusinessWater qualityEnvironmental economicsEnvironmental scienceEnvironmental planningChemistryEconomicsMembraneBiology

Abstract

fetched live from OpenAlex

Safe drinking water is essential for public health, especially in rural communities where access to reliable water sources is limited. Ensuring water quality is critical to preventing waterborne diseases and supporting sustainable development. Community-based organizations (CBOs) are vital in managing rural water needs, yet they face significant groundwater quality issues. This study analyzed water quality parameters, revealing variability in Water Quality Index (WQI) classifications, which ranged from excellent to unsuitable, necessitating targeted treatment solutions. Principal component analysis (PCA) identified three key groundwater issues: brackish water with high fluoride, turbid water, and saline water. Among the treatment options, selective electrodialysis (SED) proved most effective for fluoride-enriched brackish groundwater. Over a 12 month s period, SED consistently met Sri Lankan drinking water standards, overcoming challenges such as membrane fouling. This technology offers a sustainable solution for improving groundwater quality in CBOs, thereby enhancing public health and ensuring safe drinking water in rural Sri Lanka.

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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.027
GPT teacher head0.293
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2025
Admission routes1
Has abstractyes

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