Sediment Quality Assesment by Using Geochemical Index at Saguling Reservoir West Java Province Indonesia
Bibliographic record
Abstract
Heavy metal pollution is one of the problems that continue to occur in Indonesia which is very important because it is non-degradable, persistent, and can accumulate in the bodies of living things. Heavy metal in the water is usually present in low concentrations but human activity shall increase. Saguling Reservoir is one of the largest manmade lake in West Java Province which has experienced water contamination. The purpose of this study is to assess the quality of sediments related to the pollution of four heavy metals ie Cd, Cr, Cu, and Pb contained in Saguling Reservoir sediment using CF, MPI, Igeo and PERI methods in the rainy and dry seasons.Sediment samples are taken in twelve points around the reservoir by 2015-2017. Based on the results of this study it can be concluded that the sediment quality of Saguling Reservoir has been contaminated by heavy metals Cd, Cr, Cu, and Pb. This is caused by human activity in the water catchment area of the reservoir. Based on the results of the analysis of sediment quality using Igeo and CF the sediment of Saguling Reservoir has been polluted by heavy metals, specifically Cd in the rainy and dry seasons. The result of assessment of sediment quality by MPI method can be concluded that the sediment of Saguling Reservoir as a whole has been contaminated by Cd, Cr, Cu, and Pb. Based on calculations using PERI method, the sediment quality of Saguling Reservoir has contaminated Cd with serious ecological risk category during rainy and dry season, contaminated with Cr and Pb with low grade ecologogical risk category during rainy and dry season, has been contaminated with Cu with low grade ecological risk in the rainy season and moderate ecological risk in the dry season. Based on the results of this study that the Cd must be cautioned carefully, because of the highest concentration in the rainy and dry season than three other heavy metals.
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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) | 0.001 | 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".