Fitoremediasi Logam Besi (Fe) dan Mangan (Mn) pada Air Limbah Pengolahan Tambang Emas Rakyat di Desa Pancurendang dengan Genjer (Limnocharis flava)
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
Pertambangan emas di Desa Pancurendang, Kecamatan Ajibarang, Kabupaten Banyumas, Provinsi Jawa Tengah termasuk ke dalam pertambangan emas rakyat. Pertambangan emas rakyat secara tradisional dapat menyebabkan dampak negatif yaitu terjadinya pencemaran bagi lingkungan hidup di sekitar area pertambangan karena dalam proses pengolahannya masih menghasilkan air limbah. Air Limbah pengolahan emas di Desa Pancurendang mengandung pengotor berupa logam Besi (Fe) dan Mangan (Mn) dengan kandungan melebihi baku mutu yang telah ditetapkan. Hal tersebut berbahaya bagi lingkungan dan masyarakat di sekitar lokasi pengolahan emas. Oleh karena itu, penelitian bertujuan untuk untuk mengurangi kandungan Besi (Fe) dan Mangan (Mn) pada air limbah dengan fitoremediasi menggunakan tanaman genjer (Limnocharis flava) sistem batch dan menentukan desain pengolahan air limbah Metode analisis menggunakan perhitungan efektivitas penurunan. Penelitian ini menggunakan 3 variasi media yaitu 100% air limbah dengan netralisasi, air limbah dengan netralisasi 5 hari dan 100% air limbah tanpa netralisasi. Hasil uji laboratorium menunjukkan kandungan Besi (Fe) sebesar 866,7 mg/L dan Mangan (Mn) sebesar 206,83 mg/L. Uji coba fitoremediasi dengan sistem batch menggunakan tanaman genjer memiliki penyerapan logam Besi (Fe) paling efektif pada sampel tanpa netralisasi dengan nilai efektivitas 99,168%, sedangkan penyerapan logam Mangan (Mn) yang paling efektif pada sampel netralisasi 5 hari dengan nilai efektivitas 68,24%.Kata Kunci : Pertambangan, Emas, Air Limbah Pengolahan Emas, Fitoremediasi, Besi (Fe), Mangan (Mn)
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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