Faktor-Faktor Kerentanan dan Upaya Mitigasi Bencana Banjir di Sub-Daerah Aliran Sungai, Kasus: Kecamatan Tangse, Kabupaten Pidie
Bibliographic record
Abstract
Kecamatan Tangse adalah salah satu kecamatan di Kabupaten Pidie yang berada pada Pengunungan Bukit Barisan dengan kondisi karakteristik topografi wilayah berkontur yang beragam menjadikan Tangse memiliki daerah akumulasi genangan (cekungan) sehingga Kecamatan Tangse menjadi daerah rawan bencana banjir. Banjir menyebabkan korban jiwa, kerugian material dan rusaknya infrastruktur. Tujuan penelitian ini yaitu mengetahui faktor-faktor kerentanan bencana banjir di Kecamatan Tangse Kabupaten Pidie dan upaya mitigasi bencana banjir. Jenis penelitian yang dilakukan yaitu kualitatif deskriptif dengan menggunakan variabel kerentanan (fisik, sosial, ekonomi dan lingkungan). Menggunakan metode analisis skala likert dengan pendekatan rasionalisme bersumber pada teori dan kebenaran empirik. Hasil dari penelitian ditemukan 7 faktor yang berpengaruh secara signifikan ialah faktor curah hujan (10,7%), kelerengan (8,8%), lokasi atau jarak rumah ke sungai (8,7%), selanjutnya di ikuti dengan faktor jenis tanah, kondisi sungai, kepadatan bangunan, dan material bangunan. Upaya mitigasi yang dilakukan dalam bentuk mitigasi non struktural dan mitigasi struktural.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".