Tingkat Kerawanan Banjir di Sebagian Sub-DAS Kedungbener, Kecamatan Alian, Kabupaten Kebumen, Jawa Tengah
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 terjadi pada akhir tahun 2020 dalam lingkup sub-DAS Kedungbener yang secara administrasi terletak di Kecamatan Alian, Kabupaten Kebumen, Jawa Tengah. Curah hujan yang tinggi sebesar 250 mm/hari menyebabkan sungai Kedungbener meluap dan mengakibatkan dampak kerugian pada masyarakat sekitar. Penelitian dimulai dengan pengambilan data menggunakan metode purposive sampling. Parameter yang digunakan diantaranya adalah kemiringan lereng, elevasi, kapasitas infiltrasi, curah hujan, penggunaan lahan, dan kerapatan sungai. Analisis data menggunakan metode skoring dan pembobotan serta overlay, dilakukan untuk menentukan zona serta tingkat kerawanan banjir. Hasil analisis yang diperoleh diantaranya terdapat 3 kelas kerawanan yang di daerah penelitian, diantaranya adalah tingkat kerawanan sedang dengan luasan 273 hektar (16,8310%), tingkat kerawanan tinggi dengan luasan 587 hektar (36,1899%), serta tingkat kerawanan sangat tinggi dengan luasan 762 hektar (46,9790%).Kata Kunci: Analisis Spasial; Sub-DAS Kedungbener; Kerawanan Banjir
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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