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%.
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.
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 | 0.000 | 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".