Comparison of Water Quality Index at Intakes of Water Treatment Plants in Baghdad City
Bibliographic record
Abstract
The studying of water quality (WQ) is to determine the competence of water source for different uses. Water Quality Index (WQI) is a mathematical device used to translate huge data for water testes to simple number, This number gives comprehensive idea to the water source quality level. In this study, many samples from selected points of Tigris river stage within the intakes of eight Water Treatment Plants (WTPs) of Baghdad city were collected and tested during (2009-2010) , These (WTPs) arranged according to itsposition from the north of Baghdad city to it’s south respectively as follows (Karkh ,Tigris– East ,Wathba, ,Karama ,Qadisiya ,Dora ,Wahda and Rashid in the south).Twenty parameters were tested for an average one sample of each parameter in each month within the year(2009-2010).Canadian Council of Ministry of the Environment (CCME,2001) procedure was used to determine (WQI) of the raw water in the intake of these (WTPs). Results showed that the best (WQI) was in the intake of Al-Karkh and the worst was in Al- Rashid (WTP). Another comparison for the suitability of the raw water in irrigation purpose was tested by comparing the average of each (T.D.S and EC. ) for a one year with the criteria of each of American Salinity Library (ASL) and with Russian classification(R.C), and the results showed high concentrations of salinity , so the irrigated soil with this raw water needs good drainage system.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".