Evaluation of a Number of Water Treatment Plants in Kirkuk Governorate using the Water Quality Index
Bibliographic record
Abstract
A study was conducted on sixteen water purification plants in Kirkuk governorate to evaluate the treatment of water in them, where physical and chemical tests were conducted for raw water and treated water for a period of (6) months from December until May. Temperature, turbidity, pH, Total Dissolved Solid (TDS), Electric Conductivity (EC), alkali, Total Hardness (TH) and calcium (Ca+2) were measured. Water quality index Canadian method (CCME) was used to classify raw water quality and treated water. The results showed that the raw water for all stations was classified as category (4) (bad) during the study period. The treated water was different for the treatment plants. Two of the treatment plants recorded good efficiency in water treatment (AL-Shallalah plant and Sin AL-Thiban) the treated water remained in category (2) (good). While the water quality of AL-Mosanaa plant indicated that there was a problem in the treatment of water in this plant, the treated water remained in category (4) bad during the study period. Water quality index fluctuated for other plants during the study period. The study also showed that alkali values of all stations were higher than the allowable limit for raw water and treated water.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".