GIS-Based Seismic Damage Estimation: Case Study for the City of Kelowna, BC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study integrates risk assessment tools for diagnosis of urban areas against seismic disasters (RADIUS) and geographic information system (GIS), hence forth denoted as GBR (GIS based RADIUS). The GBR is applied for seismic damage estimation of city of Kelowna, in the interior of British Columbia, Canada. Ground-shaking intensity in the area was developed utilizing the seismic source zones defined by the Geological Survey of Canada and opinions from the local experts. Building inventories were compiled by aggregating data from municipal databases as well as sidewalk surveys and surveys through Google Maps. The GIS tool came in to be handy to provide a basis for effective decision making and gauge the vulnerable areas. Estimated damage and damage distributions were mapped on a block-by-block (5×5 km) basis. The assessment revealed that an earthquake scenario of M8.5 in the Cascadia Zone may potentially damage around 58 buildings within the city, causing 12 injuries. Plus, the study showed some damage assessment for the lifelines, for example, road and water pipelines networks. The assessment results further revealed that the city of Kelowna downtown area was expected to suffer the highest amount of damage, which in turn may produce the highest amount of economic loss, because it is the concentration of concrete high-rise buildings and clustered economic activities. Therefore, for good measure, extra meticulous efforts and razor-sharp insight bundled with precise seismic damage estimation (2-×2-km grids) were conducted for the downtown area to provide guidelines for emergency response. The proposed GBR framework provides a useful tool to quickly assess the expected damages in response to a major seismic event, which can be updated easily during disaster.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it