Identification of Wild Grass as Remediator Plant on Artisanal Gold Mine Tailing
Bibliographic record
Abstract
Tailings, waste processing from gold ore separation (amalgamation), is generally disposed of on agricultural land, so the land became polluted and unproductive. Remediation of contaminated land can use wild plants as a potential agent of phytoremediation. Surrounding the mine area are found various kinds of wild plants that grow well and potentially as remediator plants. This study aimed to obtain the kinds of plants that grow around the gold mining activities of the people and has the potential as a crop Remediator. The study was conducted in the areas surrounding the gold mine of the people in Pesanggaran, Banyuwangi District East Java Province. Exploration carried out using wild plants transect method. Plants that had the highest IIV value has the life skills and high adaptability. Soil analysis results in tailings disposal site showed a low content of soil fertility, such as pH 7.7 to 7.9 (alkaline), C-organic (0.14%), N (0.13%), P (5.7 mg kg-1), K (0.11 me/100g), and CEC (13 me/100g). The content of heavy metals Hg and Pb were detected has exceeded the threshold value (NAV) is required, which is 251.2 mg kg-1 and 135.2 mg kg-1. The results showed that the people around the gold mine site, there are about 31 species of wild plants that have adapted, 6 species of plants which have potential as Remediator. ie Eleusine inica, Chromolaena odorata, Ageratum conyzoides, Amaranthus, spp., Sesbania grandiflora and Momordica charantia, with IIV 22.27%, respectively 22.02%, 15.20%, 14.57%, 13 , 97%, and 12.49%.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".