An Assessment of Heavy Metal Contamination in the Nakdong River Around the Weir
Bibliographic record
Abstract
In this study, the concentrations and characteristics of copper (Cu), zinc (Zn), and lead (Pb) contamination in sediment samples were investigated using aqua regia extraction and Tessier’s five-step sequential extraction. Based on the concentration of metals, the influence of the Hapcheon-Changnyeong weir on sediments in the Nakdong River was assessed. The origins of the contaminants, their bioavailability, and their mobility were determined using sequential extraction. Greater concentrations of heavy metals were found in samples collected closer to the weir. The largest proportion of Cu was identified in the residual fraction based on sequential extraction, whereas Zn was predominantly found in the reducible fraction. Iron-manganese in the reducible fraction of Zn has the potential to leach back to the water body. In addition, the combined concentration of fractions 1 and 2 of Cu comprised more than 20% of total amount that still has potential to affect the water quality. The results of this study were compared with existing sediment standards set out by the NIER (National Institute of Environmental Research), Canada, and US EPA (United States Environmental Protection Agency) guidelines, as well as the risk assessment code (RAC). The concentrations of heavy metals exceeded the standards set by the Canadian guideline by up to four times in particular samples, highlighting the need for continual monitoring.
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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".