Assessment of heavy metals in sediments and aquatic organisms in West Lake (Ho Tay), Hanoi, Vietnam
Bibliographic record
Abstract
Abstract West Lake (Ho Tay) is the largest freshlake in Hanoi, Vietnam. It has a surface area of ≈ 500 ha and a total volume of >107 m3, and is 1–3.5 m in depth. West Lake has been classified as needing protection because of its valuable freshwater genetic resources. Noting that it has been extensively affected by pollution, the objective of the present study was to examine the extent of heavy metal contamination of the sediments and commercially important biota in the lake. Heavy metal concentrations in the sediment from most of 24 samples in West Lake exceeded the Ontario Ministry of Environment Screening Level for chromium (Cr), copper (Cu), manganese (Mn), lead (Pb) and zinc (Zn) concentrations. Aquatic organisms from the lake also were found to be contaminated by heavy metals. The average Zn concentrations in snails and mussels tissues ranged between 174 and 415 µg g−1, and the Pb concentrations between 3.5 and 5.2 µg g−1. The Cu concentration in shrimp (52 µg g−1) and snail (189 µg g−1), and the Pb concentration in all fish species and shrimp from the lake (2.0–5.2 µg g−1) exceeded the Food Standard Australia‐New Zealand (FSANZ) health standards for human consumption. The Cd concentration in Cyprinus carpio also exceeded the FSANZ standard. The implications of these study findings for the effective management of the food and ecological resources of West Lake are discussed.
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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) | 0.001 | 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".