Geochemical Evaluation of Arsenic Content in Different Types of Soils in Kirkuk Governorate/Northern Iraq
Bibliographic record
Abstract
In this study, 32 soil samples were taken from a depth of (0-20) distributed over different areas (Kirkuk City, Riyadh City, Hawija City, and Al-Abbasi City). They were sent to Canada to conduct chemical analyzes in Acme Lab laboratories.) using an ICP-MS device. The research aims to study the geochemical distribution of arsenic in the soil of the study areas, as well as to study the relationships between arsenic and some major oxides, as well as the degree of acidity (pH) and organic matter (OM), by calculating the correlation coefficient and factor analysis. analysis). It was found that the arsenic element is associated with a positive significant relationship with magnesium oxide (MgO), a negative significant relationship with sodium oxide (Na2O) with a value of (-0.454), a positive significant relationship with potassium oxide with a value of (0.47), and a strong positive relationship with the element cobalt (Co) with a value of (0.454). 0.474). According to the results, the average concentration of arsenic in the soil of the study area (Kirkuk, Riyadh, Hawija, and Abbasid) in the fine clay part reached (9.23 ppm), which is higher than its concentration in the coarse parts represented by silt and sand, with a value amounting to (5.83 ppm and 5.72ppm) respectively. The highest concentration of arsenic as in the soil of (MK12), which represents the site of Hay Al-Nasr Hospital, was (8.2 ppm), which is higher than the concentration of the element according to the reference value. When comparing the concentration of arsenic in the soil of the study areas with the international standards (WHO, 2021) and (EPA, 2017), it was found that it was lower than in those areas.
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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 | 0.001 | 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".