Analysis of Landslide Using Resistivity Method in the Avalanche Area of Tolnaku, Kupang Regency, East Nusa Tenggara, Indonesia
Bibliographic record
Abstract
Tolnaku, a village situated in the Kupang Regency, East Nusa Tenggara, Indonesia, is an area susceptible to landslides.An investigation into the likelihood of slope failures in this locality is imperative for landslide mitigation.This study endeavors to elucidate the subsurface conditions and evaluate the physical parameters and contributing factors that could precipitate landslides in this territory.The method of geoelectric resistivity, employing the Schlumberger configuration, has been applied to discern the varieties of rocks and cavities present in this area.The data amassed through the resistivity survey has been processed and analyzed to delineate the subsurface strata and assess their stability.The findings indicate that clay and limestone are the predominant materials in this area, and are deemed unstable.Furthermore, cavities and voids were identified in this area, amplifying the probability of landslide occurrences therein.Ultimately, the Safety Factor (SF) was ascertained to be less than 1, robustly suggesting that the slopes are perilous and vulnerable to landslides.To alleviate landslide risks in Tolnaku, Continuous Monitoring, Infrastructure Strengthening, Community Education, Coordination with Local and National Authorities, and the Development of Spatial Regulations are advocated for the future.
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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 | 0.001 | 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) | 0.000 | 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 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".