PENENTUAN PREMI MURNI DI KABUPATEN KEPAHIANG PROVINSI BENGKULU DENGAN MEMPERHITUNGKAN PELUANG KEJADIAN GEMPA BUMI DAN RASIO KERUSAKAN BANGUNAN
Bibliographic record
Abstract
Indonesia is a country that is very vulnerable to earthquakes, one of which is in Bengkulu Province, especially Kepahiang Regency. To deal with risks or losses caused by earthquakes, insurance can be purchased. Therefore, in this study, earthquake insurance premiums will be determined by taking into account the probability of an earthquake occurring and the ratio of damage to buildings in Kepahiang Regency. The PSHA (Probabilistic Seismic Hazard Analysis) method is used to determine the probability of an earthquake occurring. In the PSHA process, earthquake data will be collected and analyzed to identify earthquake sources, characterize earthquake sources, and calculate earthquake hazard (the probability of an earthquake). Damage data on buildings will be processed to obtain a ratio of building damage. After that, a pure premium will be obtained, by multiplying the EADR (Expected Annual Damage Ratio) value with the sum insured of the building, where EADR is the estimated level of annual damage due to earthquakes in an area
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".