MASTERPLAN SISTEM PENGELOLAAN AIR LIMBAH DOMESTIK DI WILAYAH PERKOTAAN KABUPATEN SUKOHARJO
Bibliographic record
Abstract
Di Indonesia, air limbah domestik merupakan pencemar terbesar yang masuk ke badan air.Pemantauan dan pengendalian air buangan dapat dilakukan salah satunya denganmeningkatkan pelayanan dalam hal sanitasi. Pemerintah menetapkan target terhadap tahun2015-2019 antara lain 100% capaian pelayanan akses air minum, 0% proporsi rumah tanggayang menempati hunian dan permukiman tidak layak (kumuh) di kawasan perkotaan dan 100%capaian pelayanan akses sanitasi. Perencanaan masterplan ini bertujuan untuk menyediakanfasilitas sanitasi yang memadai dalam pengelolaan air limbah domestik terutama di wilayahperkotaan Kabupaten Sukoharjo. Masterplan ini akan mengkaji aspek teknis-teknologis dalamperencanaan pengelolaan air limbah domestik. Pada masterplan ini direncanakan akandibangun 3 buah IPAL skala perkotaan dan 8 buah IPAL skala permukiman besar yang akanmelayani 20% penduduk perkotaan Kabupaten Sukoharjo. IPAL skala perkotaan direncanakanterdapat 2 buah di Kecamatan Grogol dan 1 buah di Kecamatan Kartasura. IPAL skalapermukiman besar direncanakan terletak di Kecamatan Kartasura, Kecamatan Gatak, 2 buah diKecamatan Baki, masing-masing 1 buah di Kecamatan Bendosari, Gatak, Polokarto danKecamatan Sukoharjo. Selain itu, untuk pelayanan sistem setempat direncanakan 3 (tiga)daerah pelayanan IPLT yang dibangun secara bertahap. Tahap pertama yaitu optimalisasi IPLTEksisting (IPLT Mojorejo), tahap kedua yaitu pembangunan IPLT di Desa Bekonang KecamatanMojolaban, dan tahap ketiga pembangunan IPLT di Desa Grajegan Kecamatan Tawangsari.IPLT direncanakan dapat melayani sekitar 70% penduduk perkotaan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.019 |
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".