Geoaccumulation Index and Enrichment Factor of Arsenic in Surface Sediment of Bukit Merah Reservoir, Malaysia
Bibliographic record
Abstract
An investigation study was conducted in Bukit Merah Reservoir (BMR) for the assessment of arsenic concentration in the surface sediment in 23 sampling stations. The sediment samples were digested and analysed for arsenic using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). Sediment parameters such as pH (4.42 ± 0.71), redox potential (121.77 ± 42.45 mV), conductivity (205.7 ± 64.07 μS cm–1) and organic matter (25.35 ± 9.34%) were also examined. The main objectives of this study are to determine the arsenic distribution and concentration and at the same time to assess the enrichment of arsenic using the geoaccumulation index (Igeo) and enrichment factor (EF). This study shows the total arsenic concentration in the surface sediment of BMR is 4.302 ± 2.43 mg kg–1 and found to be below the threshold value of Canadian Interim Sediment Quality Guidelines (ISQG). High arsenic concentration is recorded near the southern part of the lake where anthropogenic activities are prevalent. Based on Igeo, 13% of sampling stations are categorised as moderately polluted, 52.2% as unpolluted to moderately polluted and the rest is categorised as unpolluted. EF shows 78.3% stations are classified as extremely high enrichment and the rest as very high enrichment. This finding provides important information on the status of arsenic contamination in BMR and creating awareness concerning the conservation and management of the reservoir in the future.
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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 | 0.000 | 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".