Study the concentration of heavy metals in the North Yaran oilfield sediments in the Hur Al-Azim wetland, Khuzestan
Bibliographic record
Abstract
Sediments are the main absorbers of metal pollutants. Therefore, it is essential to study them. In this paper, 72 samples of surface sediments from six stations were selected using Grap sampler in the summer of 2015 to determine the concentration of heavy elements such as cadmium, nickel, lead, and vanadium in the surface sediments of the North Yaran oilfield in Hur Al-Azim wetland. An atomic absorption device was used to determine the concentration of heavy elements, and SPSS and EXCEL software were used for statistical data processing. According to the results, the average concentration of cadmium, lead, vanadium, and nickel was 1.4, 48.87, 32.65, and 87.33 mg/L, respectively. Sediment quality standards in the United States (NOAA) and in Canada (ISQGs) were used to compare these values with the allowable contamination levels of elements in sediment. According to the results, the average concentration of cadmium and lead in the present study was higher than the standard indicators of ERL and TEL and lower than the standard indicators of ERM and PEL. Average concentration of nickel was higher than these standard indicators. The results showed that nickel has more pollution than other metals, consistent with other research conducted in the region. Based on results, the amount of pollution of heavy metals studied in this research was not dangerous and critical. Still, it is necessary to prevent the increase in metallic toxicity in the region's environment by adopting preventive approaches.
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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) | 0.000 | 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".