Water quality assessment of the upper Euphrates River basin using NSF and CCME indices in western Iraq
Bibliographic record
Abstract
Many developing countries with river systems face persistent challenges related to water pollution, complicating efforts to meet safe drinking water standards. This study evaluates the water quality of the Euphrates River along the Anbar Governorate using two widely recognized models, including NSF Water Quality Index (NSF-WQI) and CCME Water Quality Index (CCME-WQI) developed by the Canadian Council of Ministers of the Environment. Seven monitoring sites were selected along the upper Euphrates basin, from Al-Qaim to Fallujah. Seventeen key parameters were analyzed, including physicochemical and biological indicators. Both indices produced similar classifications at upstream locations (Al-Qaim, Al-Haditha, and Al-Baghdadi), indicating marginal water quality, which reflects limited suitability for direct human use without treatment. CCME-WQI values were consistently lower than NSF-WQI results, indicating a more stringent assessment approach. The results align with documented pollution trends linked to urban and agricultural practices, particularly in highly populated regions. The study concludes that both models are effective for assessing water quality; however, the CCME-WQI provides greater flexibility and wider applicability across diverse environmental conditions due to its capacity to accommodate a broader range of parameters and site-specific considerations. In contrast, the NSFWQI demonstrates increased sensitivity to particular input parameters. These findings can enhance strategies for managing water resources and controlling pollution along the Euphrates River.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".