Assessment of the supplied water quality for Sulaimani, Iraq as a case study using CCME method
Bibliographic record
Abstract
Water quality index (WQI) is an important technique used for the evaluating and classifying water quality of supplied water, by summarizing large number of parameters from the result of variety tests of water into less number and terms Water quality index (WQI) value can be determined. Management of water quality is not so easy and simple, it needs to work with a lot of data. In this study evaluating of water quality of (Sulaimani city, KR, Iraq) is presented by using Canadian Council of Ministers of the Environment (CCME). The study conducted by monitoring long term water quality data over past eleven years, collected from 17 sampling points, tanks from which potable water supplied to all of the city residents, located in the (Sulaimani city, KR, Iraq). To obtain the CCME WQI value twelve parameters used : PH, Total dissolved solid (TDS), Chloride (Cl-1), Calcium (Ca), Sodium (Na), Potassium (K), Magnesium (Mg), Total hardness (TH), Total Alkalinity, Chloride (Cl2), Electric conductivity (EC), Sulphate (SO4-2).The obtained patterns present different water quality situations, WQI values ranged from good to excellent, based on the result obtained and analyzed using the from of WQI, it is revealed that the value of WQI ranged from (82) to (94) over the past 11 years, it means the main source of water supplied to the city which is Dokan lake is relatively high and clean as it is over a mountain watershed 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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".