PENGELOLAAN SAMPAH RUMAH TANGGA DESA SUKALUYU KARAWANG MELALUI REDUCE, REUSE, DAN RECYCLE GUNA MENDORONG PERILAKU HIDUP BERSIH DAN SEHAT
Bibliographic record
Abstract
Sungai Citarum masuk ke dalam salah satu sungai terkotor di dunia versi Blacksmith Institute pada tahun 2013. Desa Sukaluyu adalah salah satu desa yang bersinggungan langsung dengan sungai citarum, dimana jumlah timbunan sampah yang tidak terangkut mencapai 24.761 m3/hari. menngetahui gambaran pengetahuan dan sikap masyarakat terkait program 3R. Tahapan persiapan dengan metode penyebaran kuesioner dan windshield survey, dan pelaksanaan dengan metode sosialisasi edukasi,429 kuesioner terkumpul menunjukkan sebesar 94% warga di desa Sukaluyu berada pada usia produktif dengan mayoritas warga tingkat pendidikan yang tinggi (79%), pengetahuan masyarakat sudah baik. pengetahuan dan sikap mengenai program pengolahan sampah di ligkungan juga menunjukkan potensi yang baik, bahkan warga bersedia kerja bakti (89.5%) dan sangat siap jika harus membayar iuran untuk pengolahan sampah (95.1%). Namun, warga yang belum terpapar sosialisasi tentang pengolahan sampah (45.9%)., serta edukasi melalui sosialisasi 3R. Program 3R di desa Sukaluyu Karawang masih belum berjalan dengan merata. Sehingga diharapkan semakin banyak penyuluhan dan pendampingan seputar program 3R demi terwujudnya citarum harum.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".