Water quality assessment and phosphorus effect using water quality indices: Euphrates River- Iraq as a case study
Bibliographic record
Abstract
Most rivers in developing countries are facing water contamination problem. Therefore, saving water quality by complying with the industrial, drinking, and agricultural allowable standard limits has been difficult. This study aims to assess Shatt Al-Kufa water quality as one branch of the Euphrates River by calculating three types of water quality indices in two cases, excluding and including the phosphate (PO4) consentration, as it was the parameter that most met the standard. The used water quality indices are the Weight Arithmetic Water Quality Index (WAWQI), the Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) and the Oregon Water Quality Index (OWQI). Fifteen parameters were analyzed, including pH value, Biological Oxygen Demand, Turbidity, Total Hardness, Orthophosphate, Sulphate, Nitrate, Alkalinity, Potassium, Sodium, Magnesium, Chloride, Dissolved Oxygen, Calcium and Total Dissolved Solids. The results show that the average WAWQI for three stations, including PO4, were 33.79, 43.75 and 37.62, which is good water. However, in excluding PO4, the water quality was characterized as very poor depending on the resulting values (86.62, 88.86 and 91.91, respectively). The CCMEWQI values for three stations were 63.83, 60.40 and 55.69, including PO4, so the water quality was fair and marginal. According to OWQI, the water quality for three stations was very poor in two cases since the OWQI value less than 59. Pearson correlation shows a good link, especially total hardness and total dissolved solids with salt.
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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 | 0.001 |
| Open science | 0.000 | 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".