KAJIAN RISIKO BENCANA LONGSOR KECAMATAN LOANO KABUPATEN PURWOREJO
Bibliographic record
Abstract
Kecamatan Loano berada di bagian timur-selatan Kabupaten Purworejo, merupakan daerah rawan longsor yang disertai kondisi sosial ekonomi yang menambah kerentanannya. Studi ini bertujuan untuk mengkaji risiko bencana longsor, cakupan kajian meliputi karakteristik ancaman, kerentanan {sosial ekonomi & fisik lingkungan, kapasitas (respon lingkungan)} serta risiko bencana. Pengolahan data menggunakan software ArcGIS dan SPSS. Secara fisik alam, ancaman risiko longsor bervariasi yaitu 21,36 km2 (40,09%) indeks rendah, 13.14 km2 (24,66%) sedang dan 18,78 km2 (35,25%) tinggi. Indeks kerentanan sosial ekonomi & fisik lingkungan terdiri dari 3 desa indeks rendah, 11 desa sedang dan 7 desa tinggi. Karakteristik kapasitas (respon lingkungan) diambil dengan menggunakan kuesioner individu masyarakat dan pemerintah desa. Karakteristik kapasitas (respon lingkugan)-nya adalah 14 desa indeks rendah, 4 desa sedang dan 3 desa tinggi. Kerentanan secara holistickyang merupakan jumlah total kerentanan sosial ekonomi & fisik lingkungan serta kapasitas (respon lingkungan) terdapat 3 desa indeks rendah, 4 desa sedang dan 14 desa tinggi. Tingkat risiko bencana longsor bervariasi, terdiri dari rendah 6,72 km2 (12,62%) , sedang 24,59 km2 (46,15%) hingga tinggi 21,96 km2 (41,23%). Risiko longsor yang tinggi perlu dikurangi dengan menurunkan kerentananya. Selanjutnya, diharapkan metode ini dapat diterapkan di wilayah lainnya yang memiliki karakteristik hampir sama dengan Kecamatan Loano.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".