Lead Pollution in the Angke KapukMangrove Forest of the Jakarta Bay Area
Bibliographic record
Abstract
Marine tourism is authorized in Jakarta Bay’s Angke Kapuk mangrove forest. Maritime vessel activities, maintenance, and land reclamation can pollute nearby aquatic environments and sedimentary deposits. This study examines lead (Pb), a heavy metal, in water and sediment samples to measure contamination. Lead pollution in aquatic habitats can harm aquatic organisms and humans through bioaccumulation in the food chain. The sampling was done twice in August 2023, seven days apart. This technique was done at three stations with different activities. Microwave Plasma-Atomic Emission Spectroscopy (MP-AES) was used to measure lead amounts in the samples. The water sample analysis showed 0.0022-0.0092 mg/L, matching Indonesian Government Regulation No. 22 of 2021 standards. Conversely, sediment samples showed 0.067-0.200 mg/kg, which is below the quality criteria set by ANZECC&ARMCANZ in 2000 for Australia and New Zealand and CCME in 2001 for Canada. Despite low pollution according to recognized criteria, heavy metals in ecotourism zones require government and public attention. Additional information, in-depth research on water contamination, and heightened awareness of the impacts of heavy metals may be necessary.
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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".