A web application for rapid seismic risk assessment
Bibliographic record
Abstract
Numerous computer models have been developed for seismic loss analyses at urban and regional scales.They seem, however, ill-suited to custom application to the specific Canadian hazard and exposure settings and, more importantly, inadequate for utilization by the broader non-expert public safety community.Therefore, communication of the potential seismic risk results to local stakeholders, such that they can properly understand their exposure and vulnerability, represents an outstanding challenge.The objective of the present study is to describe the methodological background and ongoing development activities of the Rapid Risk Evaluator (ER2), a relatively rapid and user-friendly risk assessment application, developed to overcome the current communication barriers between risk experts and decision makers.Developing ER2 included: pre-computing site-specific databases containing ground motion scenarios, prediction of potential attenuation with distance and local site amplification, a standardized inventories of buildings' structural properties and occupancy categories, and assessment of the seismic vulnerability using hazard-compatible vulnerability functions.These functions correlate directly the intensity of the seismic shaking to the probability of damage and direct economic and social losses.This approach allows for conducting risk scenarios in large urban centers within minutes.The above approach was programmed into an easy to run web-application.Equipped with graphic user interface, ER2 allows non-expert users to run otherwise complex seismic risk scenarios through a simple intuitive selection process.An example of ER2 applied to a hypothetical earthquake event in Quebec City is included to illustrate the simplicity of the user interface and capabilities of the application.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.300 | 0.232 |
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".