Research on earthquake disaster risk based on the anti-lethal levels of buildings at the township level: a case study in Suqian, China
Bibliographic record
Abstract
The assessment of earthquake disaster risk constitutes a fundamental basis for the rational allocation of disaster relief resources and the formulation of scientifically strategies for earthquake prevention and disaster mitigation. This study, through a case study conducted in Suqian City, introduces a methodology for evaluating regional earthquake disaster risk based on the analysis of the anti-lethal levels of buildings. Initially, indicators of the anti-lethal levels of buildings for three prevalent building types are identified in Suqian, including frame structures, brick-concrete structures, and brick-wood structures. Subsequently, the anti-lethal levels of buildings at 359 survey points across 98 towns in Suqian are quantified through field investigations of ‘town-to-town’. And then, the overall anti-lethal levels for each of the 98 townships are calculated. Finally, by integrating seismic hazard levels, permanent residential population, and personnel mortality matrix, the earthquake disaster risk for Suqian is comprehensively assessed. The result shows that although the overall quality and anti-lethal levels of urban buildings are better, urban areas of Suqian face higher earthquake disaster risk than rural areas due to elevated hazard potential and higher population density. The method offer more precise and actionable technical support for the development of local earthquake disaster prevention and mitigation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".