Mitigasi bencana banjir melalui normalisasi Daerah Aliran Sungai Beringin dan pemanfaatan flood early warning system di Kelurahan Mangkang Wetan
Bibliographic record
Abstract
<span id="docs-internal-guid-febeadee-7fff-6350-068b-b10445c75749"><span>Mitigasi bencana merupakan upaya meminimalkan korban jiwa dan harta benda mulai dari pencegahan, kesiapsiagaan hingga pengurangan kerentanan. Normalisasi sungai dan Flood Early Warning System (FEWS) adalah bentuk mitigasi bencana struktural dan nonstruktural yang dilakukan dalam menghadapi bencana banjir. Penelitian ini bertujuan untuk mengidentifikasi permasalahan penyebab banjir dan menganalisis bentuk mitigasi bencana banjir yang dilakukan di Kelurahan Mangkang Wetan berupa normalisasi sungai dan FEWS. Metode yang digunakan adalah analisis komparatif penanganan banjir di wilayah yang sudah melakukan praktik baik dengan kemungkinan aplikasinya di Kelurahan Mangkang Wetan untuk memberikan rekomendasi dalam peningkatan mitigasi. Hasil penelitian menunjukkan bahwa masih ada masyarakat yang tinggal di kawasan sangat rawan bencana, namun perangkat keras FEWS yang dapat memperkuat mitigasi justru kurang terawat. Di samping itu sudah terlihat upaya tata kelola kolaboratif dari pemerintah, NGO, dunia usaha, dan masyarakat Kelurahan Mangkang Wetan.</span></span>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".