Evaluation the Quality of Raw and Treated Water for Number of Water Treatment Plants in Baghdad, by Using Canadian Model for a Water Quality Index
Bibliographic record
Abstract
Laboratory tests on some physical and chemical properties were conducted to evaluate the quality of drinking water on some water treatment plants in Baghdad (Al-Qadisiya, Al-Dora, Al-Wahda and Al-Rasheed), these samples were taken from raw and treated water. The measurements of taken every month and for eight years in order to evaluate<strong> </strong>the drinking water quality and efficiency of these plants. The quality of drinking water was calculated by using 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 mg/L, fluoride, Nitrate, chloride, color, conductivity. The samples were taken from the treated water effluent from 2005 to 2013. The study showed that the range of water quality index for the raw water is (49-54) and can be classified as a bad water and it's needing advanced treatment, while the water quality index of treated water was (77,78, 70, 67) for<strong> </strong>(Al-Qadisiya, Al-Dora, Al-Wahda and Al-Rasheed) respectively. Therefore, the water quality index of treated water of (Al-Qadisiya, Al-Dora, Al-Wahda and Al-Rasheed) can be classified within the third category (moderate). DOI: <a href="http://dx.doi.org/10.25130/tjes.24.2017.35">http://dx.doi.org/10.25130/tjes.24.2017.35</a>
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".