Heavy Metal Concentration (Pb, Cu, Cd, Zn) In Water And Sediments In Serangan Waters, Bali
Bibliographic record
Abstract
Heavy metal pollution can disturb aquatic ecosystems and indicator of water quality. This study aims to determine the concentration of heavy metals (Pb, Cu, Cd, Zn) in water and sediments. This study was conducted in the waters of Serangan, South Denpasar District, Bali in June-August 2019. The research with each location taking 8 points of water and sediment sampling and measuring the quality of its waters. Water samples taken at the surface of the water as much as 330 ml and sediment samples taken as much as 500 grams. The method used is wet ashing preparation and analyzed by means of AAS (Atomic Absorbsion Spektrophotometri). The results of the average analysis of heavy metal concentrations in water are Pb 0 mg / L, Cd 0.438 mg / L, Cu 0 mg./L, and Zn 0.284 mg / L. The results of the analysis are compared with the standard quality value of sea water that the heavy metals Cd and Zn have exceeded the threshold. While the average concentration of heavy metals in sediments are Pb 17.444 mg / kg, Cd 18.093 mg / kg, Cu 113.981 mg / kg, and Zn 53.505 mg / kg. The concentration of Cd and Cu heavy metals exceeds the limits set by the ANZECC / ARMCANZ quality guidelines and the Canadian Council of Ministers of the Environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 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 teacher head, 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".