Identification of Landslide-Prone Areas Using the Horizontal to Vertical Spectral Ratio (HVSR) Method and the GIS Approach in Semakai District, Tanggamus Regency, Lampung Province
Bibliographic record
Abstract
Landslides are one of the natural disasters that frequently occur in Indonesia and can result in loss of life, property, and environmental damage. Semaka Subdistrict, Tanggamus Regency, Lampung Province, is located in a landslide-prone area. The aim of this research is to analyze the geological characteristics and soil layer dynamics for landslide mitigation in the Semaka area. This study uses a scoring method based on three parameters: slope inclination, sediment thickness, and peak ground acceleration (PGA), to determine site class and create a landslide-prone zone map in the Semaka region. Microtremor data is analyzed using the Horizontal to Vertical Spectrum Ratio (HVSR) method. The obtained data represents ground vibrations as a function of time, with a dominant frequency range in the Semaka area between 2.18 and 13.48 Hz and sediment thickness ranging from 10 to 80 meters. The maximum PGA values range from 100 to 600 gal. The seismic sources used in the PGA map are from the subduction zone and Semangko Fault. Based on the slope values, geological factors such as sediment thickness, and PGA values, the villages of Sedayu and Sukaraja are identified as the areas most susceptible to landslides. The findings of this research are expected to enhance landslide control measures in the Semaka region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".