Integration of Disaster Imagination Game and Geographic Information System Methods in Mapping Earthquake Disaster Risk in Botteng Village, Mamuju Regency
Bibliographic record
Abstract
This study aims to map the risk of earthquake disasters in Botteng Village, Mamuju Regency, after the devastating earthquake measuring 6.2 on the Richter scale that hit the area on January 15 2021. The earthquake caused heavy damage, around 90% of the 350 houses were seriously damaged, causing many fatalities and injuries. This research uses a combined methodology from the Disaster Imagination Game (DIG) and Geographic Information Systems (GIS) to conduct a comprehensive earthquake risk analysis in the region. This participatory approach enables community involvement in identifying vulnerabilities and assessing risk levels while utilizing spatial analysis to effectively visualize data. The research results show that most of the Botteng Village area has a high risk of earthquake disasters, namely 1,507.13 hectares potentially at high risk, 978.94 hectares at medium risk, and 663,177 hectares at low risk. These findings underscore the critical need to increase public awareness and disaster preparedness, as well as improve mitigation strategies for effective disaster risk reduction in the future. The implications of this research are very significant for local governments and disaster management institutions, in mitigating disasters by integrating community perspectives with GIS technology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".