Evaluasi TPA Temesi Berdasarkan Penilaian Indeks Risiko Lingkungan di Desa Temesi, Kecamatan Gianyar, Kabupaten Gianyar, Provinsi Bali
Bibliographic record
Abstract
TPA Temesi telah beroperasi selama 28 tahun yang awalnya direncanakan dengan metode lahan urug saniter,tetapi kegiatan di TPA sempat menggunakan metode lahan urug terkendali selama beberapa tahun kemudianberalih menggunakan metode penimbunan terbuka. TPA Temesi memiliki luas 4,5 hektar dengan sampah yangmasuk sebanyak 420 ton/hari. Semakin meningkatnya timbulan sampah yang tidak dibarengi oleh penyediaansarana dan prasarana yang memadai dan hanya mengandalkan sistem penimbunan terbuka, mengakibatkan bebansampah menumpuk di TPA. Kurang baiknya pengelolaan sampah di TPA Temesi dapat menimbulkan risikobahaya terhadap lingkungan sekitar. Penelitian ini bertujuan untuk mengetahui hasil evaluasi tingkat bahaya TPATemesi berdasarkan penilaian Indeks Risiko Lingkungan. Metode penelitian yang digunakan yaitu metodesurvey dan pemetaan, metode uji laboratorium, metode wawancara, dan metode pembobotan. Metodepembobotan mengacu pada penilaian Indeks Risiko Lingkungan berdasarkan Peraturan Menteri PekerjaanUmum RI Nomor 03 Tahun 2013. Hasil penelitian TPA Temesi didapatkan nilai indeks risiko lingkungan sebesar550,945 yang termasuk dalam kategori tingkat bahaya sedang dengan tindakan yang disarankan TPA diteruskandan direhabilitasi menjadi lahan urug terkendali secara bertahap.Kata Kunci: TPA Temesi; Rehabilitasi; Indeks Risiko Lingkungan; Controlled Landfill; Sampah
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".