Evaluation of Groundwater Using The Water Quality Index (WQI) In Hawija Area, Kirkuk, Northern Iraq
Bibliographic record
Abstract
The water quality index (WQI) has been used to comprehend the Hawija region's groundwater quality for drinking purposes. Where Some locals solely utilize groundwater for drinking purposes. Forty groundwater samples were collected from the Hawija region's bore wells. The groundwater was somewhat hard and slightly alkaline. These materials were transported to (Acme Lab) to Canada for examination. In addition to comparing the findings of the current study with Iraqi requirements, the World Health Organisation (WHO) and Environmental Protection Agency (EPA) classification of water quality and its suitability for various uses, this paper also examines physical properties such as pH, electrical conductivity, temperature, dissolved salts, and chemical properties, including estimating the water content of major ions. In the low-flow season, the WQI values varied from 29.96 to 112.5, whereas in the high-flow season, they ranged from (25.61 to 142.32). Out of 40 groundwater samples, 12 (30%) were deemed to have excellent water quality, 17 (42.5%) were deemed to have bad water quality, 10 (25%) were deemed to have extremely poor water quality, and 1 (2.5%) were deemed unfit for drinking during the low flow season. Groundwater samples taken during the high flow season had a water quality rating of 16 (40%) good, 14 (35%) bad, 7 (17.5%) extremely poor, and 3 (7.5%) unfit for drinking. This suggests that much of the research area's groundwater samples are unsuitable for human consumption.
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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 | 0.001 | 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".