Evaluation of heavy metal contamination in sediments of the Linggi River, Malaysia
Bibliographic record
Abstract
This study evaluates the concentrations and contamination levels of selected heavy metals (Mn, As, Cr, Fe, Zn, and Co) in surface sediments from seven locations along the Linggi River, Malaysia. Using Instrumental Neutron Activation Analysis (INAA), elemental concentrations were quantified, and contamination indices were applied to assess ecological risks. Results showed that arsenic (As) exhibited extremely high enrichment, with EF values ranging from 40.02 (S1) to 70.79 (S7), and Igeo values from 4.42 to 5.06, categorizing it as heavily to extremely polluted. Chromium (Cr) showed moderate enrichment (EF: 1.77–2.55; Igeo: –0.34 to 0.26), while zinc (Zn) had minor enrichment (EF: 1.01–1.51; Igeo: –0.92 to –0.57). Manganese (Mn), cobalt (Co), and iron (Fe) exhibited EF and Igeo values below 1 and 0, respectively, indicating minimal anthropogenic influence. The Pollution Load Index (PLI) values ranged from 1.02 (S5) to 1.37 (S7), confirming pollution across all sites, with S7 being the most contaminated. When compared against Canadian and Consensus Freshwater Sediment Quality Guidelines, arsenic and chromium levels exceeded Threshold Effect Levels (TEL) and Threshold Effect Concentrations (TEC), indicating significant ecological risk. These findings highlight the need for urgent environmental monitoring and pollution control in the Linggi River system.
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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".