Analisis Tingkat Risiko Banjir pada Daerah Aliran Sungai (DAS) Bialo Provinsi Sulawesi Selatan
Bibliographic record
Abstract
Daerah Aliran Sungai (DAS) secara sederhana dapat diartikan sebagai salah satu wadah yang memiliki fungsi mengalirkan air hujan ke danau atau laut. Perubahan pemanfaatan lahan di hulu DAS Bialo yang menyebabkan pendangakalan menjadi salah satu penyebab terjadinya banjir di area hilir DAS Bialo. Tujuan penelitian ini adalah menganalisis tingkat ancaman (hazard), kerentanan (vurnerability), kapasitas (capacity) dan risiko (risk) banjir pada Daerah Aliran Sungai Bialo. Metode pengumpulan data dalam penelitian ini menggunakan metode wawancara, dokumentasi, observasi, dan studi literatur. Metode analisis yang digunakan dalam penelitian ini adalah metode deskriptif kuantitatif, analisis spasial, dan deskriptif kualitatif. Metode deskriptif kuantitatif digunakan untuk melakukan analisis ancaman (hazard), kerentanan (vurnerability), kapasitas (capacity) dan risiko (risk). Analisis spasial digunakan dalam proses pemodelan hasil perhitungan ancaman, kerentanan, kapasitas, dan risiko. Metode deskriptif kualitatif digunakan untuk melakukan interpretasi hasil analisis spasial. Hasil penelitian menunjukkan bahwa tingkat risiko bencana banjir pada DAS Bialo terdiri dari 3 klasifikasi yaitu rendah, sedang dan tinggi. Luas Rendah 3342,39 Ha, Sedang 6748,27 ha dan Tinggi 807,86 Ha.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".