A Survey of Heavy Metal Concentrations in the Surface Sediments along the Iranian Coast of the Caspian Sea
Bibliographic record
Abstract
Metals discharged into coastal areas of marine environments are likely to be scavenged by particles and removed to the sediments. The sediments, therefore, become large repositories of toxic heavy metals. This research examined the concentrations of heavy metals (Al, Cd, Cu, Pb, Ni, Zn) in the nearshore sediments in the alongshore direction of the Iranian coast of the Caspian Sea. Fourteen samples were collected and granulometric compositions were determined. The consideration of three grain size fractions (0.355 mm, 0.212 mm and 0.075 mm), plus fourteen bulk samples required analyzing 56 samples for the presence of heavy metals. Laboratory analysis of the samples was accomplished using the Cold Acetic Protocol, followed by Inductively Coupled Plasma Optical Emission Spectroscopy. Preparation of the samples involved the utilization of the Cold Acetic Acid Extraction Protocol established by the Great Lakes Institute for Environmental Research (GLIER), Canada. The results provided evidence of large differences in total metal concentrations in the sediment samples from the fourteen sites. Box and Whisker plots demonstrated that metal concentrations were not homogeneously distributed, and that there were large spatial variations in the median concentrations of heavy metals at each sample site. The statistical technique of discriminant analysis revealed that the six heavy metals had distinct and statistically significant concentrations at various locations along the coast. Concentrations reflected metal loadings from anthropogenic sources located at and in the vicinity of the sampling sites.
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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 | 0.000 | 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".