STRATEGI PENGURANGAN RISIKO BENCANA BANJIR PADA DAS KIRASA, KABUPATEN BULUKUMBA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bencana banjir kerap terjadi hampir disetiap wilayah, termasuk DAS Kirasa yang terletak di Kabupaten Bulukumba. Bencana banjir yang pernah terjadi di DAS Kirasa menimbulkan kerugian yang cukup besar terhadap masyarakat, sehingga tujuan penelitian ini adalah untuk menganalisis tingkat risiko bencana banjir di DAS Kirasa, Kabupaten Bulukumba, serta merumuskan strategi untuk mengurangi risiko bencana banjir di DAS Kirasa. Analisis risiko bencana benjir adalah metode analisis yang digunakan kemudian diolah menggunakan aplikasi ArcGis 8.10. Hasil analisis menunjukkan bahwa tingkat risiko bencana banjir pada DAS Kirasa terbagi dalam 2 kategori yaitu rendah dan tinggi. Untuk tingkat risiko rendah, desa/kelurahan yang termasuk yaitu Barombong, Bonto Sunggu, Polewali, Taccorong, Bialo, dan Balibo. Sedangkan untuk tingkat risiko tinggi yaitu Dampang, Bukit Tinggi, Polewali, Taccrong, Paenre Lampoe, Kalumeme, Ela-Ela, Caile, Terang-Terang, dan Loka. Untuk mengurangi tingkat risiko bencana banjir diperlukan strategi berupa optimalisasi kinerja kelompok tanggap bencana, pemerataan dana bantuan penanggulangan bencana, optimalisasi dan pemeliharaan sarana dan prasarana serta implementasi aturan penanggulangan bencana dalam mengurangi risiko bencana.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it