Water Quality Assessment of the Tigris River in Baghdad Using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI)
Bibliographic record
Abstract
The objective of this study was to assess the water quality of the Tigris River in Baghdad and its northern areas through the application of the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI). The study was conducted over two distinct periods representing dry (summer) and wet (winter) seasons over a 12-month period in 2021, encompassing nine strategic sampling sites along the river from Al-Karkh to Al-Wihda. Water samples were analyzed for sixteen physicochemical parameters including turbidity, alkalinity, total hardness, major ions, nutrients, and trace metals. The study revealed that water quality ranged from poor to marginal across all sampling locations, with CCME-WQI values varying between 42.1 and 56.03 during the dry period and 42.8 to 54.97 during the wet period. The findings highlight significant spatial and seasonal variations in water quality, demonstrating the severity of anthropogenic pressures on the Tigris River. This study concludes that immediate implementation of wastewater treatment, stricter pollution control, and continuous monitoring programs are urgently needed to safeguard this vital freshwater resource that supports more than 10 million inhabitants of Baghdad.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".