DISTRIBUTION OF HEAVY METALS IN SEDIMENTS IN LAKES IN WUHAN WITH ASSESSMENT ON THEIR POTENTIAL ECOLOGICAL RISK
Bibliographic record
Abstract
Distribution and characteristics of heavy metals(Hg、Cd、Cu、Pb、Zn、As、Cr、Ni) in sediment columns from six lakes in Wuhan were investigated. The results demonstrated that the concentrations of heavy metals in lakes in urban area are invariably higher than sediments from lakes in suburb area, and the concentration of heavy metals in top sediment of the urban polluted lakes show some degree of accumulation when compared with deeper sediments in lake sediment columns, while heavy metals show no significant change among sediment columns in suburb lakes. Potential ecological risk index and fresh water sediment quality criteria were then used to assess the heavy metals ecological risk, which leld us to conclude that the potential risk order of elements were Cd Hg As Cu PbZn; Moshu Lake is of the highest potential risk, Jinying Lake ranks the second, while the others have relatively small risk. But, overall, the potential ecological risk of investigated lakes in Wuhan is light, at least not very serious. With reference to the threshold obtained by Canadian ecological databank of sediment baseline, heavy metals in sediments of some lakes of relatively higher risk index may press negative effects to biota within the lake.
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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 | 0.001 |
| 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".