Kajian Literatur tentang Model Mitigasi Bencana Lanskap Pesisir di Kota Banda Aceh
Bibliographic record
Abstract
Disaster mitigation is a disaster risk management strategy that can help reduce and minimize disaster impacts. Spatial planning policy is essential for disaster mitigation as it will affect the distribution of development and the vulnerability of communities to disasters. Communities living in coastal settlements are very vulnerable to disasters, so a spatial-based disaster mitigation strategy will provide an appropriate strategy for activities in coastal settlements, especially in the coastal areas of Banda Aceh City. The purpose of this research is to evaluate the role of spatial planning policies in supporting disaster mitigation, as well as to examine the implementation of mitigation strategies involving community participation and the use of GIS technology. This research used qualitative methods with a literature review approach to analyze, synthesize, and identify trends, gaps, and recommendations from various literatures related to spatial-based disaster mitigation, community participation, and GIS technology. The results of this study showed that spatial planning policies played an important role in reducing disaster risk, with a focus on proper zoning and protection of vital infrastructure. Adaptation strategies such as mangrove planting and effective evacuation routes were key to mitigation. Community participation and the use of GIS technology helped identify risks and develop hazard maps. However, challenges such as lack of policy socialization and limited spatial data remain obstacles to optimal implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".