Efektifitas Penyisihan Seng (Zn) Limbah Tailing Menggunakan Metode Fitoremediasi di Desa Pancurendang, Kecamatan Ajibarang, Kabupaten Banyumas, Provinsi Jawa Tengah
Bibliographic record
Abstract
Penambangan emas oleh rakyat di Desa Pancurendang, Kecamatan Ajibarang Kabupaten Banyumas dilakukan secara tradisional. Pengolahan emas dilakukan dengan metode amalgamasi dan metode sianidasi. Limbah tailing hasil pengolahan emas secara sianidasi dibuang begitu saja di lahan persawahan dengan lapisan tanah tanpa adanya pengolahan terlebih dahulu. Tujuan dari penelitian ini yaitu menghitung efektifitas metode fitoremediasi untuk menurunkan konsentrasi seng (Zn) pada limbah tailing. Metode yang diterapkan dalam penelitian adalah metode rancangan percobaan dengan fitoremediasi. Percobaan fitoremediasi dilakukan menggunakan tanaman alang-alang (Imperata cylindrica) dan diuji konsentrasi seng (Zn) pada 10, 20, dan 30 hari pemaparan pada tanah dengan variasi prosentase tailing yaitu 0%, 30%, 50%, dan 100%. Baku mutu seng (Zn) dalam tanah mengacu pada PP RI Nomor 22 Tahun 2021 sebesar 50 mg/L. Hasil percobaan fitoremediasi menunjukkan nilai efektifitas penyisihan logam seng (Zn) tertinggi pada tanah dengan prosentase tailing 50% selama 10 hari pemaparan yaitu sebesar 43,35% dengan nilai konsentrasi seng (Zn) yang telah memenuhi baku mutu yaitu 46,121 mg/kg.Kata Kunci: Penambangan dan pengolahan emas rakyat; Tailing; Seng (Zn); Fitoremediasi; Alang-alang (Imperata cylindrica)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".