PERANCANGAN SKEMA BANK AIR KALI BABEN UNTUK MENDUKUNG KEBUTUHAN AIR BERSIH DI PERUMAHAN PANCA ARGA AKMIL MAGELANG
Bibliographic record
Abstract
Perubahan iklim dan pertumbuhan penduduk telah meningkatkan tekanan terhadap ketersediaan air bersih, termasuk di kawasan Perumahan Panca Arga I Akmil Magelang. Perumahan Panca Arga dilalui jaringan irigasi Progo Manggis, yang dikenal dengan nama Kali Baben, akan tetapi belum dimanfaatkan untuk mendukung kebutuhan air bersih bagi warga perumahan. Salah satu bentuk pemanfaatan Kali Baben sebagai solusi inovatif untuk meningkatkan ketahanan air adalah dengan pengembangan “bank air”, yaitu sistem pengelolaan air berbasis pemanfaatan air Kali Baben untuk disalurkan, difilterisasi/disaring dan diendapkan kemudian diinjeksikan dan disimpan ke dalam tanah secara alami sebagai cadangan air tanah pada akuifer. Penelitian ini bertujuan merancang dan menguji sistem bank air berbasis Kali Baben dengan teknologi sederhana untuk meningkatkan ketersediaan air bersih bagi Perumahan Panca Arga dan memenuhi standar higiens Depkes RI. Metode penelitian menggunakan metode campuran diawali dengan mendesain Sistem Bank Air, mengimplementasikan dan menilai kualitas air yang dihasilkan, serta wawancara dengan penggagas bank air tersebut. Hasil penelitian menunjukkan bahwa desain skema bank air Kali Baben mampu secara efektif menyimpan air tanah tanpa mengurangi secara signifikan kuantitas aliran air Kali Baben. Skema bank air perumahan Panca Arga secara kualitas menghasilkan air yang memenuhi standar kualitas air bersih higiens Depkes RI
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".