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Record W4414070274 · doi:10.1051/e3sconf/202564802001

Appraisal of Groundwater Status Applying the CCME WQI Model

2025· article· en· W4414070274 on OpenAlexaboutno aff
Nilufar Rajabova, Alaa Farouk Aboukila, Shakhriyor Toshev, Grace Eleojo Obasuyi

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterWater qualityAridTotal dissolved solidsNitrateHydrology (agriculture)Natural (archaeology)

Abstract

fetched live from OpenAlex

Both natural and human-induced factors increasingly threaten groundwater quality in arid regions. This study evaluates the quality of shallow natural groundwater (10–25 meters deep) in the Amudarya province, Republic of Karakalpakstan, Uzbekistan. The objective was to determine whether the groundwater in this region is safe or poses potential health risks. Water samples were collected from eight different sites and analyzed for nine physicochemical parameters, including total hardness (TH), chloride (Cl - ), sulfate (SO 2- ), total dissolved solids (TDS), fluoride (F - ), iron (Fe 2+ ), nitrate (NO 3 - ), copper (Cu 2+ ), and pH. Standard laboratory techniques were employed for testing, and the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) model was applied for integrated assessment. The results revealed that several parameters — particularly TH, TDS, Cl - , and SO 4 2- — exceeded World Health Organization guidelines. The overall water quality was rated as ‘Marginal,’ indicating restricted usability and potential health risks. Furthermore, a comparative analysis of ten international studies reveals that Amudarya’s marginal WQI values fall within the lower-middle category globally, primarily due to salinization in semi-arid agroecological settings. This contrasts with regions affected by toxic metal contamination, highlighting distinct geographical and ecological drivers of water quality degradation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.036
GPT teacher head0.312
Teacher spread0.276 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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