Hydrochemistry Assessment of Surface and Groundwater Quality Using GIS and a Heavy Metal Pollution Index (HMPI) Model in a Hawija area, Kirkuk, north Iraq
Bibliographic record
Abstract
The Heavy Metal Pollution Index (HMPI) has been used to assess the quality of surface and groundwater drinking water in the Hawija region, where residents use groundwater for drinking. Forty groundwater samples were collected from the Hawija region's wells and analyzed in the Acme Laboratories in Canada. The results of this study were compared with the Environmental Protection Agency's (EPA) and the World Health Organization's (WHO) classification of water quality and its suitability for different uses. Five samples (12.5%) had low pollution levels during the low flow season, 26 samples (65%) had medium levels, and nine samples (22.5%) had high levels. Thirty samples, a mean of 75% of the total groundwater samples obtained during the high flow season, were rated as having low pollution, while ten samples (25%) were rated as having medium pollution. This shows that a large portion of the groundwater samples in the study area are impermissible for human consumption. In the low-flow season, the HMPI values ranged from 8.38 to 148.68, with a mean of 32.43 in the high-flow season, they ranged from 3.55 to 29.23, with a mean of 8.54. The HMPI values of surface water ranged from 3.75 to 77.64 and had a mean of 26.101 during the low-flow season, whereas they varied from 4.19 to 26.35 and had a mean of 11.25 during the high-flow season.
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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 | 0.001 |
| 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".