IDENTIFIKASI KARAKTERISTIK DAN FAKTOR PENGARUH PADA BENCANA LONGSOR LAHAN DI KECAMATAN DAU
Bibliographic record
Abstract
Tujuan penelitian ini untuk menganalisis dan mengetahui identifikasi karakteristik bencana longsor lahan dan identifikasi faktor pengaruh bencana longsor lahan di kecamatan Dau. Metode penelitian yang dilakukan yakni survei pada lokasi terpilih yaitu desa Kucur dan Petungsewu. Survei dilakukan dengan mengamati berbagai kondisi yang disesuaikan dengan tabel parameter faktor pengaruh terjadinya longsor lahan, selanjutnya diskoring untuk mengetahui faktor penentu pada masing-masing titik sampel pengamatan secara kuantitatif. Akumulasi dari tiap skor menghasilkan klasifikasi tingkat rawan bencana longsor lahan yang terbagi dalam berbagai kelas yaitu Kelas I dengan kriteria tingkat rawan dan paling tinggi Kelas V dengan kriteria sangat rawan. Hasil penelitian menunjukkan desa Kucur dan Petungsewu tergolong wilayah yang memiliki kerawanan longsor kelas IV dengan skor 32-36 yang terbagi di empat titik lokasi. Hasil identifikasi menunjukkan tipe longsor longsor rotasi dan translasi. Adapun faktor utama yang mempengaruhi terjadinya longsor antara lain curah hujan, jenis tanah, penggunaan lahan, dan kemiringan lereng.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".