Evaluation The Quality of Raw and Treated Water for Number of Water Treatment Plants in Baghdad, Using a Water Quality Index
Bibliographic record
Abstract
Laboratory tests were conducted to evaluate the quality of drinking water on some water treatment plants in Baghdad (AlKarkh, Shark Dijla, AlWathba, and Alkramh), the samples taken from raw (Tigris River) and treated water. The measurements of some physical and chemical properties taken every month and for eight years in order to evaluatethe drinking water quality and efficiency of these plants. The quality of drinking water was calculated byusing Canadian model index (Canadian Council of Ministers of the Environment) in water quality evaluation, as contributed thirteen variables in the index calculation: the temperature of the water, turbidity, pH, total hardness (as CaCO3), magnesium, calcium, sulfate, iron, fluoride, Nitrate, chloride, color and conductivity. The sampleswere taken from the treatedwater that outside from the plant from 2005 to 2013. The study showed that the range of water quality index for raw water is (51-57) and can be classified as a bad water and needs advanced treatment, while the water quality index of treated water was (86, 81,80,80) for (AlKarkh, Shark Dijla, AlWathba andAlkramh) respectively. The water quality index of treated water of (AlKarkh, Shark Dijla, AlWathba and Alkramh) can be classified as Category II ( good).
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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 | 0.002 | 0.003 |
| 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".