Geochemical Study of Ecological Risk Potential of Heavy Metal Contamination in Urban Lake Sediment - Malaysia - from the Context of Ecological Disturbance Theoretical Tradition
Bibliographic record
Abstract
This study quantified the degree of heavy metal contamination and ecological risk potential from metals concentration in urban lake sediment. The analytical method involved six geochemical indices (enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), degree of pollution, modified degree of contamination, & pollution load index (PLI). Sediment samples were analysed using ICP-MS. The results revealed that EF and Igeo of trace elements were in the order of Pb >Cr>Cu>Mn>Ni, whereas the order of heavy metals was Na > K > Fe > AL. Na and Pb manifested the highest level of evidence for anthropogenic enrichment and geochemical anomaly. Based on CF, the sediment is heavily contaminated by Pb, Na, and moderately by Fe, K, AL. In the event of profound ecological disturbance, and resuspension of sediment contaminants to the water column, the contamination effects of Pb, Na, Fe, K, and AL on biota will range from heavy to moderate contamination. Assessment using PLI revealed that the sediment is in 80% of locations polluted and in a progressive state of deterioration by the metals. The overall degree of metal ecological risk potentials seems higher in the northern and southern outlet parts of the lake, especially during the dry season.
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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 | 0.000 | 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".