Assessment of Lower Zab river water quality using both Canadian Water Quality Index Method and NSF Water Quality Index Method
Bibliographic record
Abstract
Rivers are considered the most important sources of surface water on Earth. They are play a significant role in all human activities and the quality of river water is needed. Therefore, the importance of the water quality index is arising through providing data base about quality of the water source, and explain the change in the water quality over a period of time continually. This study involved determination of physicochemical and biological parameters of Lower Zab river in Kirkuk city at two different points. The objectives of the study are to assess the present water quality, through analysis of some selected water quality parameters like pH, TDS, BOD, dissolved oxygen, turbidity, EC, alkalinity, and salinity etc. and to compare the results with the Canadian Council of Ministers of the Environment and National Sanitation Foundation Water Quality Indices. Raw water samples were collected from the Lower Zab river twice a month by one sample every 15 days from each station. The water quality data include 16 different parameters. Tests were carried out following the American Public Health Association standard methods. The results show that all parameters values were within the standards of drinking water proposed by the CCME standards and Iraqi standards or the World Health Organization standards for drinking purpose, except turbidity, DO, nitrate, calcium, which were mostly higher than the standards and sometimes BOD and potassium. The results of WQI showed that the water quality at LZ3 station is lower than LZ2 station due to the polluting activity of the Lower Zab river. Furthermore, for the years 2014, 2015, and 2016, the water quality was degraded due to the ISIS war. Also, it was noted in the 2013 year that the water quality degraded more in fall and winter seasons due to that the earth has exposed to the long-dried season and then suddenly exposed to a high rainfall season which in turns leads to increase some parameters very high (i.e. turbidity). Finally, the Lower Zab river water cannot be use for drinking directly. However, a pretreatment is needed before the drinking use.
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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 | 0.012 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".