Assessment of post-war groundwater quality in urban areas of Mosul city /Iraq and surrounding areas for drinking and irrigation purposes by using the Canadian Environment Water Quality Index CCME-WQI and Heavy Metal Pollution Index HPI
Bibliographic record
Abstract
The negative impact of war acts in the conflict area of the city of Mosul and its surroundings on groundwater quality and thus its use as drinking water, in domestic applications and for irrigation was addressed. Therefore, 8 wells were analyzed from January to September 2022 using the parameters pH, E.C., TDS, % salinity, COD, phosphate, nitrate, sulfate and the heavy metals Cd, Pb, Zn, Cr and Ni, and water quality was evaluated using a mathematical model based on the CCME WQI, the HPI and present salinity. Due to salinity, 6 of the 8 wells were moderately suitable for irrigation and 2 wells were difficult in use. According to the CCME WQI criteria, 4 wells were highly and 3 wells were moderately contaminated for drinking water supply and domestic use, and therefore unusable or limited usable, while 3 wells were unusable and 2 wells were moderately usable for irrigation purposes. For irrigation, only one well showed low and 2 wells showed marginal contamination. The HIP revealed good quality of 3 wells, poor quality of 2 wells and unsuitability of 3 wells (drinking water/ domestic use) or very poor quality (irrigation), respectively. According to all approaches, the wells located in the conflict area consistently showed poor water quality. Thus, war had a significant negative impact on groundwater quality in the conflict area, as the surface-near wells located here showed comparatively high levels of contaminations and heavy metals due to the infiltration of contaminated surface water, damaged sewage networks and infiltration of rainwater after passing through highly polluted soils. Cadmium, followed by lead, were the dominant water contaminants, which is why caution is advised before using this well water.
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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 | 0.002 |
| 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) | 0.000 | 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".