Assessment of the concentration of some heavy metals in bottom sediments in the coastal and island areas of the southern region, Vietnam
Bibliographic record
Abstract
Rivers, such as the Dong Nai, Saigon, and Mekong, are subject to urban, industrial, and agricultural pollution, leading to a high risk of heavy metal accumulation in bottom sediments. With the aid of a boat and bucket as tools, a survey was conducted to collect sediment samples from the coastal areas (downstream of Dong Nai, Saigon, Mekong Rivers) and islands (Con Dao, Tho Chu) in the Southern region of Vietnam during April and May 2023. Out of a total of 35 samples collected, preserved according to the guidelines of the Ministry of Natural Resources and Environment, the heavy metal indicators were analyzed by atomic absorption spectroscopy, and the average heavy metal concentrations were in the following order: Zn>Cu>Cd>Pb>As. Compared with the Vietnamese Technical Regulation (QCVN 43:2017/BTNMT) on sediment quality and the sediment quality of the Canadian Environment Ministry, the heavy metal concentrations in the research area have not exceeded the specified standards. However, compared with standards from the USA, the Russian Federation, and other regions in Vietnam, the levels are quite high. Moreover, the Igeo index indicates that the concentration of Pb, As, and Cd at many coastal locations downstream of the rivers is heavily polluted. This evidences that in addition to pollution from upstream sources, the activities of the local population significantly contribute to the accumulation of pollution. The research results provide a scientific basis for limiting pollution sources to ensure sustainable development of the studied areas, which is necessary today.
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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".