Heavy Metal Pollution in Sediments from Aquatic Ecosystems in China
Bibliographic record
Abstract
Abstract We conducted an analysis of heavy metals content, including As, Cd, Cr, Cu, Hg, Pb, and Zn in sediments from aquatic ecosystems in China measured in recent publications. Then, we evaluated the extent of heavy metal pollution in these ecosystems in seven different industrial districts in China (Dongbei, Huabei, Huazhong, Huanan, Huaxik, Xibei, and Huadong) with the potential ecological risk index. We found that Cd was the most concentrated pollutant, followed by Hg and As, while Cr, Cu, Pb, Zn were found in low concentrations in sediments from all types of aquatic ecosystem in China. Sediments collected from all seven industrial districts were heavily polluted, and the sequence, from most to least polluted was Dongbei>Huabei>Huazhong>Huanan>Huaxi>Xibei>Huadong. All four types of aquatic ecosystem were found to be seriously polluted and the sequence, from most to least polluted was: river>sea>lake>wetland. Specifically, Cd and Hg were the most serious pollutants in all four aquatic ecosystems, and As was also a serious pollutant in rivers. For the seven industrial districts studied the sea was the most polluted ecosystem in Dongbei, the river was the most polluted ecosystem in Huabei, Huanan, Huazhong, and the lake was the most polluted in Huadong, Huaxi, and Xibei.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".