GEOCHEMICAL FRACTIONATION OF TOXIC TRACE HEAVY METALS (CR, CU, PB, AND ZN) FROM THE ESTUARINE SEDIMENTS OF 5 RIVER MOUTHS AT JAKARTA BAY, INDONESIA
Bibliographic record
Abstract
Jakarta Bay is located at the north coast of Jakarta bordered by 106 03\'00\'\' Longitude and 6 10\'30\'\' Latitude. Administratively bordered by Bekasi Regency on the east and Tangerang Regency on the west. There are 13 -19 rivers flow to the bay with 2050 industries that produce hazardous waste, including heavy metals. Metal concentrations in surface sediments and their spatial distributions have increased, recently. Concentration of Pb during 10 years period increase from 23.3 mg kg-1 to 118.2 mg kg-1. The objectives of this study is to know the distribution of Chromium (Cr), Copper (Cu), Lead (Pb), and Zinc (Zn) of Jakarta Bay, Indonesia and their geochemical partition in marine sediments that are bound to âExchangeable Fractionâ, âReducible Fractionâ, âFe-Mn Oxide Fractionâ, âOxidize able Fractionâ, and âResidual Fractionâ. The result showed that the concentration of heavy metals in the sediments in most locations were above the Canadian Standard for Contaminated sediments. Concentration of Cr ranged from 48.68—292.09 ppm, Cu ranged between 18.62—151.82 ppm, Pb ranged from 39.7—303.42 ppm, and Zn ranged between 165.83—487.69 ppm. Standard for Cr, Cu, Pb, and Zn are 22 ppm, 30 ppm, 25 ppm, and 60 ppm, respectively. Percent fraction of Cr in labile fraction (F1, F2, and F3) ranged from 30-60 %, while for Cu, its percent fraction mostly bound to lithogenic fraction as much as 38–78%. Percent of labile fraction of Pb ranged from 22-54 %, while for Zn as much as 15-72%. These meant that not only Cr but also Pb and Zn were possible to be easily released in the environment as bioavailable metals for biota, especially, benthic invertebrates
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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 | 0.001 | 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 | 0.001 |
| 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".