Analysis of heavy metals (Pb and Zn) concentration in sediment of Blanakan fish ponds, Subang, West Java
Bibliographic record
Abstract
Blanakan fish ponds receive water resource from Kali Malang and Blanakan rivers. Industrial and domestic activities along the river can cause pollution, especially heavy metals. Zinc (Zn) is an essential element that needed by an organism, while Lead (Pb) is a nonessential element that is not needed. Discharge of waste water from industries and anthropogenic activities continuously not only pollute the water but also the sediment and biota live on it. This research was aimed to know the heavy metals content in the sediment of Blanakan fish ponds. Sediment samples were taken on July and August 2016 at three locations. Heavy metals were analyzed using Atomic Absorption Spectrophotometry (AAS) Shimadzu 6300. The result of Lead (Pb) measurement showed that Fish Pond 1 had higher average concentration compared Fish Pond 2 and Fish Pond 3 which was 0.55 ppm. Standard for Lead (Pb) in sediment according to Ontario Sediment Standards (2008) is 31 ppm. Based on Zinc (Zn) measurement, it was known that average of Zinc (Zn) concentration also higher on Fish Pond 1 compared to Fish Pond 2 and 3 which was 1.93 ppm. According to Ontario Sediment Standards (2008), a standard for Zinc (Zn) in sediment is 120 ppm. This indicated that heavy metals in the sediment of fish ponds were below standards. Statistical analysis using t-test showed that there was no significant difference of heavy metals content among fish ponds.
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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 | 0.001 | 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".