Groundwater quality assessment for the wells system in Zurbatiyah, Iraq, for civil and irrigation uses by two water quality index approaches
Bibliographic record
Abstract
Groundwater quality in the Zurbatiyah sub-district, eastern Iraq, was assessed using the arithmetic water quality index (AWQI) and the Canadian water quality index (CCME-WQI).Field data were collected from six wells over a five-month period, and twelve physico-chemical parameters were analyzed.AWQI scores ranged from 5.46 to 84.77, classifying water quality from "excellent" to "poor", depending on the well and season.In contrast, CCME-WQI scores ranged from 49.5 to 58.6, with all wells classified under the "marginal" category, indicating frequent exceedances of permissible limits.The findings reflect high spatial and temporal variability, with parameters such as EC (1.750-6.120µS/cm) and TDS (805-4.590mg/L) often exceeding national and international guidelines.These results suggest moderate to severe salinization, particularly during peak irrigation months.Overall, CCME-WQI was found to provide a more conservative and realistic assessment of water quality risk, while AWQI tended to overestimate quality under certain seasonal conditions.The study highlights the need for continuous groundwater monitoring and sustainable water management in semi-arid regions.Based on Iraqi and FAO standards, none of the wells were suitable for drinking, while only two were deemed conditionally suitable for irrigation purposes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".