Heavy Metal Assessment in Taps Drinking Water of Ramadi City Using Water Quality Indices, Anbar Province, Iraq
Bibliographic record
Abstract
This study goals to assess the concentrations of specified Heavy Metals (HMs) and quality of taps drinking water of Ramadi city, western Iraq. Heavy Metal Pollution Indices like heavy metal pollution index (HMPI ), heavy metal evaluation index (HMEI) and contamination degree (CD) were applied to assess the supplied water. The average concentrations of Lead (Pb), Nickel (Ni), Chromium (Cr), Arsenic (As) and Cadmium (Cd) in most stations exceed the maximum admissible concentration, while Iron (Fe) in most of stations was within the maximum admissible concentration according to local and global guidelines. (HMPI ) values of most stations were exceed the maximum critical value of 100. (HMEI) values of most stations were exceed the value of 10 recommended for drinking water. (CD) values of most stations were exceed the value of 1 recommended for drinking water. The pollution origins were assessed using principal component analysis (PCA) and clustering analysis (CA). The results indicate that contamination comes from anthropogenic causes being the most common and lithogenic sources being the least common. The present concentration of (HMs) in taps water is causing health and environmental problems, water with high (HMs) concentrations would need to be treated before being supplied to consumers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".