Study of water and sediment surface quality on defilement of heavy metals Pb & Cd at a downstream section of Musi River, South Sumatera, Indonesia
Bibliographic record
Abstract
This research (1) analyzed the content of Pb and Cd in water and sediment; (2) analyzed physical and chemical parameters in water; (3) determined the enrichment factor and accumulation index of Pb and Cd in sediments; and (4) to identified water quality based on the Pollution Index (IP) and STORET. The analysis was carried out using PCA (Principal Component Analysis) and Pearson's correlation, and was conducted in the downstream section of the Musi River, Palembang, using a random sampling technique. Based on the analysis, it was found that the content of Pb and Cd in the waters had surpassed the quality standard whereas the contents of both heavy metals found in the sediment still met the quality standard. It was also found that several variables of the physical and chemical parameters did not meet the criteria. Based on the Enrichment Factor (EF) and the Accumulation Index (Igeo) of Pb and Cd, the river had not been defiled. The pollution index values of both IP and STORET indicated that the water had been heavily polluted. Based on the PCA, three factors which affected the quality of the river water were found. Keywords: abiotic, defilement, evaluation, index, mud, quality.
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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 | 0.000 | 0.000 |
| 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".