Application of the Canadian Index (CCME WQI) to Assess Water Quality for Aquatic Life: A Case Study of Water Quality for the Khoser and Tigris Rivers in the North of Nineveh Governorate
Bibliographic record
Abstract
The present study aimed to assess the water quality of the Khosr and Tigris rivers for aquatic life by applying two mathematical models: the model proposed by Rodriguez de Bassoon and the Canadian model (CCMEWQI). Physical, chemical, and biological properties were estimated to calculate the values for these models. The results indicate that the Khosr River has poor water quality for aquatic life, with Canadian model values ranging between 30.9 and 41.5. In contrast, the Tigris River water at the comparison site was classified under the Marginal water category, with quality further deteriorating after the Khosr River merges with it, showing values between 52.7 and 40.2. Similarly, using Rodriguez de Bassoon’s model, the Khosr River was classified as having Bad water quality with values ranging between 26.35 and 35.96, while the Tigris River exhibited Medium water quality with values between 54.81 and 64.33. The deterioration in water quality was mainly attributed to a significant reduction in oxygen levels, reaching as low as 1.2mg/ L, coupled with an increase in organic load to 56mg/ L. This reflects a considerable degradation of the Khosr River, which negatively impacts the water quality of the Tigris River within the Mosul City area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".