Analysis of the Vulnerability of the Villages of Eshtehard CityAgainst Earthquakes
Bibliographic record
Abstract
According to its geographical and geological location, Iran is among the ten earthquake-prone countries in the world. This is despite the fact that now about a quarter of Iran's population lives in rural settlements, and on the other hand, the establishment of villages near or on active fault lines is necessary to be scientifically investigated in terms of the level of physical vulnerability. Therefore, the purpose of this research is to analyze the vulnerability of the villages of Eshtehard city against earthquakes. The current research method is descriptive-analytical and library and field methods were used to collect information and data. Data analysis and research information were done through spatial analysis (Arc Map) and statistical analysis (SPSS) and the statistical population includes 1326 rural households from 4 Eshtehard districts, based on non-probability sampling and with a purposeful (judgmental) method, the number of 208 The household was selected as the sample size. The findings of the research show that first: most of the rural areas are located in the safe and low-risk areas of the region, half of which are currently uninhabited and the rest have a very low population density, in contrast to a small number of rural areas Villages with high population density are located in high-risk areas. Second: Among the 8 criteria selected to estimate the vulnerability of housing against earthquakes, it can be said that in the studied area, the indicators of the width of the roads, the quality of the materials used, and the density of the residential units are not in a suitable condition and make them vulnerable. Rural houses play an important role against earthquakes, and against the indicators of building age and the number of floors of residential units, they have favorable conditions and have moderated the vulnerability of housing in four villages to some extent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".