Introducing the Rapid Earthquake Damage Estimation (RED-E) System for Improved Life Safety Outcomes During Earthquake Early Response in Canada
Bibliographic record
Abstract
In the wake of a major earthquake in Canada, responders can expect to encounter critical gaps in situational awareness in the first 48-72 hours that will hamper effective decision-making. To address this challenge, Natural Resources Canada is developing the Rapid Earthquake Damage Estimation (RED-E) system. This modelling system aims to produce maps of structural, human, and economic impacts within tens of minutes of a significant seismic event, similar to the United States Geological Survey's PAGER product but with enhanced details specific to Canada. This paper presents our research on optimizing the RED-E system through the User-Centered Design approach. End-user consultation throughout the development of RED-E will ensure that its outputs are practical and actionable for first responders, emergency managers, and infrastructure operators. Key findings from initial consultations underscore the need for immediate post-earthquake situational awareness, although complete understanding may take days to weeks. End-users expressed a preference for RED-E outputs in diverse formats, with road disruption modelling and secondary hazard assessments being particularly valuable. This study outlines the essential requirements for the outputs of RED-E and documents initial prototypes, showcasing the potential of the system to transform early post-seismic emergency response efforts across Canada, aiding in prioritization and resource allocation until ground-truth data become available.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".