Pengembangan Teknologi Energi Terbarukan Terpadu Melalui Pemanfaatan Mikrohidro dan Biogas Komunal Pada Kawasan Tertinggal Desa Gelang Kabupaten Jember
Bibliographic record
Abstract
Desa Gelang merupakan salah satu desa yang terletak di lereng Gunung Argopuro, Kabupaten Jember. Kondisi geografis Desa Gelang khususnya Dusun Lanasan memiliki perkebunan teh dan kopi seluas ±5.205,245 Ha. Walaupun Dusun Lanasan memiliki agrowisata menarik, tetapi tidak adanya listrik mengganggu aktivitas perekonomian dan mobilitas warganya. Selain itu, warga Dusun Lanasan yang memiliki 32 sapi dan 60 kambing menghasilkan limbah kotoran ±800 Kg per-hari dimana limbah kotoran hanya ditumpuk sehingga terlihat kotor dan menyebarkan bau tidak sedap. Melalui pengabdian masyarakat ini diterapkan solusi mikrohidro dengan memanfaatkan aliran sungai di Dusun Lanasan dan pembuatan biogas komunal. Pelaksanaan kegiatan dimulai dari pengukuran potensi mikrohidro, konstruksi bangunan sipil, pembuatan mikrohidro dengan turbin cross-flow, pembuatan saluran inlet-outlet dan digester biogas, serta instalasi listrik dan biogas. Hasil kegiatan ini dibangun PLTMH Dusun Lanasan yang menghasilkan daya listrik 2800 Watt untuk kebutuhan listrik dari 20 KK dan biogas komunal dari pengolahan limbah ternak mampu menghasilkan 16 m3 biogas per-hari yang telah disalurkan ke dapur-dapur warga Dusun Lanasan dimana mampu menghemat pembelian 2-3 elpiji per-bulannya.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".