Post-Landslide Liquefaction Analysis: A Case Study in the Kupang Regency Area, Indonesia
Bibliographic record
Abstract
Landslide disasters in Indonesia often cause significant damage to public facilities, agricultural lands, and result in human casualties.This study was conducted to deeply understand the dynamics and causes of landslides and to develop effective disaster mitigation strategies in areas vulnerable to soil movement.The focus of the research is on the rotational landslide incident that occurred on the Trans Timor National Road KM 73, Noelmina Village, Takari District, Kupang Regency, East Nusa Tenggara Province, on February 17, 2023.Through a methodological approach that includes geomorphological analysis, evaluation of hydrological conditions, and land use study, this research identifies the main factors causing the landslide, including the changeable nature of Bobonaro clay soil, increased soil saturation due to hydrological conditions, and increased water load in the basin area due to land use.The findings emphasize the importance of efficient hydrological management, careful land use planning, and strengthening the capacity of local communities in landslide disaster mitigation.Recommendations for disaster mitigation include the development of infrastructure that is resistant to soil movement, vegetation planting for slope stabilization, and community education on sustainable land use practices.This research provides a significant contribution to disaster mitigation efforts in Indonesia by highlighting an approach based on a comprehensive understanding of the causes of landslides and effective adaptation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".