Evaluating the water quality of al-Shatrah stream using the Canadian Water Quality Index (CWQI) in Dhi Qar Governorate
Bibliographic record
Abstract
This study investigates the pollution factors present in the water of the Al-Shatrah Stream, focusing on identifying the health risks associated with these factors. To achieve this objective, the researcher employed the Canadian Water Quality Index (CWQI) as a tool for classifying and examining the impact of spatial and seasonal variations on pollution levels. The study analyzed nine indicators, including physical and chemical parameters such as temperature, turbidity, pH, total dissolved solids, total suspended solids, chlorides, sulfates, and alkalinity. Additionally, a bacteriological analysis was conducted to assess the presence of fecal coliform bacteria at three sites along the stream during the months of February and July. The researcher employed statistical analysis to calculate the arithmetic mean and standard deviation of the measured values, both temporally and spatially. Variations between sites were assessed using analysis of variance (ANOVA), and the least significant difference test was applied at a significance level of 0.05 for all indicators. The findings from the Canadian Water Quality Index (CWQI) analysis are concerning, as they indicate that the water quality of the Al-Shatrah Stream is subpar and unsuitable for human consumption. The CWQI equation, a widely accepted and reliable method for assessing water quality, has classified the sampled sites along the stream as having poor water quality.
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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 | 0.000 |
| 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".