Evaluasi Penyerapan Kadar Logam Pada Daun Tanaman Wetland Pasca Pengolahan Limbah Cair Tenun
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it