A Comprehensive Review of Holistic Indicators for Seismic Vulnerability Assessment of Malaysia
Bibliographic record
Abstract
Due to the complex nature of seismic vulnerability assessment, different approaches and data are required, based on the country.Alternatively, seismic vulnerability assessment can be categorized into two common techniques, the conventional and holistic methods, the use of which depends on the region's conditions.Generally, conventional methods concern the consequences of an earthquake by estimating the potential loss caused by the structural inventory damage and the number of casualties.Meanwhile, holistic methods focus on the different primary factors that contribute to seismic vulnerability, which are represented by the social, economic, physical, and environmental elements of a community or structure in a region.However, less attention has been given to the quantitative evaluation of holistic seismic vulnerability in Malaysia compared to hazardrelated research.Therefore, the aim of this study was to identify the holistic seismic vulnerability indicators in the context of an earthquake in Malaysia.Analysis is critical for understanding the numerous indicators of causes of earthquakes to define their relative relationships and the disaster risk probability.Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting method, a comprehensive review of the Scopus and Web of Science databases was undertaken to search for indicators with a substantial impact on the aforementioned dimensions of earthquake vulnerability.This article concludes that there are three major elements of vulnerability (exposure, resilience, and coping capacity), comprising eighteen indicators of seismic vulnerability, in the context of earthquakes in Malaysia.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".