Evaluasi Penyerapan Kadar Logam Pada Daun Tanaman Wetland Pasca Pengolahan Limbah Cair Tenun
Bibliographic record
Abstract
Water pollution can be caused by an increase in the number of industries, one of which is the textile industry. One effort to reduce water pollution by heavy metals is by utilizing absorption by plants. This research aims to determine the concentration of metal pollutants Cr, Cu, Cd, and Pb accumulated by Vetiveria zizanioides plants. The test method in this research was wet digestion using a nitric acid solution (HNO3) which was then analyzed using an Atomic Absorption Spectrophotometer (AAS). Based on research results, the average metal absorption concentration of copper (Cu), chromium (Cr), Lead (Pb), and cadmium (Cd) after processing using the FTW system with the help of bacteria in processing the highest metal content is Pb 0.0007 (mg /Kg dry weight) and for Cu it is 0.0001 (mg/Kg dry weight) and Cd metal is 0.00002 (mg/Kg dry weight) and finally Cr is 0.000001 (mg/Kg dry weight). And for processing using the FTW system without the help of bacteria in processing, the average metal absorption concentration of Cu, Cr, Pb, and Cd, the highest metal content at Cu 0.0001 (mg/Kg dry weight) and Pb 0.0003 (mg/ Kg dry weight) for Cr and Cd were not detected. The absorption of Cu, Cr, Pb, and Cd metals did not affect plant growth.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".