Assessment of heavy metals pollution indices in sediments of Tiyab and Kolahi International Wetlands
Bibliographic record
Abstract
Heavy metals are pollutants from multiple man-made or natural sources which directly or indirectly enter bodies of water. Therefore, investigation of deposits as metal contaminants is important. The international wetlands of Tiyab and Kolahi are among the most important ecosystems in the south of Iran that due to development programs are polluted by different sources. In order to identify environmental pollution of heavy metals in the sediment of Tiyab and Kolahi, we have collected 22 surface sediment samples from 11 study sites using the Grap Sampler. In order to determine the toxicity and limit pollution index of elements in the sediment, we used the Sediment Quality Standard of America and the Canadian Sediment Quality Standard was used. The results showed that the concentration of heavy metals had the following average ppm: cadmium (6.15), lead (23.22), nickel (142.8) and copper (36.18). The accumulation index of Muller regarding the level of contamination of the area in question indicated that the pollution level of the wetlands was at a medium level. Due to the concentration of heavy metals and the index findings, it could be concluded that cadmium contamination could be related to oil and anthropogenic pollution.
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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 | 0.002 | 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".