Appraisal of Groundwater Status Applying the CCME WQI Model
Bibliographic record
Abstract
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 (Fe2+), nitrate (NO3-), copper (Cu2+), 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 SO42- — 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".