Mangroves Sediment Ability as a Traps of Heavy Metal Chrome in Tukad Mati Estuary, Bali – Republic of Indonesia
Bibliographic record
Abstract
Tukad Mati is one of the ten rivers has decreased due to the development of water quality screen printing and textile industries. Monitoring of the waters of the Tukad Mati has so far been confined to the Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) only. Whereas monitoring should be carried out thoroughly considering the heavy metal content is also harmful to users of water resources in Tukad Mati. One of the natural buffer of heavy metals in sediments of mangrove waters is a final media gathering all kinds of heavy metals waters, while mangrove itself is a natural sediment trap.Therefore, the heavy metals in the waters will experience the highest accumulation in mangrove sediments. The purpose of this study is as early monitoring to detect the chromium metal in waters and sediments in Tukad Mati estuary. In this study conducted in on-site data measurements in the form of data pH, temperature, salinity, disolved oxygen (DO) and discharge water. measurement of chromium content in the water and sediment of Tukad Mati estuary using AAS method.The study showed that from the results of on-site data measurements in estuary of Tukad Mati showed that the average value of salinity, pH, temperature and Dissolved Oxygen still meet quality ministerial decree of the environment of Indonesian Republic no 51 in 2004 for biota and marine tourism. The measurement results also showed that chromium content in water an average of 0.19 (mg/l) and exceed the ministerial decree of the environment of Indonesian Republic no 51 in 2004. Mangrove and non-mangrove sediments in Tukad Mati estuary have been exceed the Lowest Effect Level for organism and some sediment contaminated with chromium and passed the quality standards According to Canadian Council of Ministers of Environmental in 2000. Chromium concentration in sediment higer than water allegedly caused by heavy metals that tends to bind and fall to the bottom waters along the process of sedimentation.
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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 | 0.000 |
| 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) | 0.001 | 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".