Water quality assessment in dry regions using statistical methods
Bibliographic record
Abstract
Water demands have increased even more in recent decades because of the high population density. Surface and groundwater resources are insufficient to meet these demands. As a result, governments have turned to the treatment of sewage water. Sewage water contains multiple types of contamination, creating a major health risk. In the research region, 48 water samples were obtained, including 18 samples of surface water and 30 samples of groundwater. The Canadian Council Water Quality Index (CCWQI) program calculates the water quality index to evaluate the water quality for drinking and human use. The World Health Organization (WHO) and the Egyptian Ministry of Health (EMH) determined regulatory limits for drinking water and each value of the investigated parameter connected with them. According to the findings, 79% of the tested water samples are safe to drink and are excellent for human and wildlife use. Due to infiltration or recharging of groundwater with drainage water, as well as the involvement of dissolution, leaching processes, and anthropogenic activities that damage human health, animals, and some plants, these samples are unfit for drinking and domestic consumption. The heavy metal level of Cd and Pb in the examined water samples was found to be above WHO and EMH acceptable limits. Furthermore, due to oral exposures, the examined water samples may cause complex health concerns such as non-carcinogenic and carcinogenic influences for children over adults due to a reduction in children's immunity. As a result, water treatment should be carried out in the examined region to protect the health of the residents.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".