Strategy for Mitigating Landslide Disaster Risk through Improving the Status of Disaster Resilient Villages (DESTANA) Case Study in Kemuning Village, Ngargoyoso District, Karanganyar Regency
Bibliographic record
Abstract
Kemuning Village is one of the areas with the highest potential for landslides in Ngargoyoso District, Karanganyar Regency. This service activity aims to help institutional strengthening of the Tangguh Disaster Village (Destana) in disaster planning at the village level in the context of increasing preparedness and disaster management of landslides. The activity was carried out in Kemuning Village with an implementation time of 1 month The research method through open interviews, field observations, document review, and then analyzed descriptively qualitatively. The results of the disaster resilience assessment indicate that Kemuning Village is included in the category of Main Disaster Resilient Village. Based on the assessment analysis presents indicators of disaster management systems, disaster risk management, quality and basic access in the village is still lacking so it needs to be improved. The target of service outcomes obtained from this activity is to optimize the role of disaster resilient villages to reduce the risk of landslide disasters.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".