Introducing the Rapid Earthquake Damage Estimation (RED-E) System for Improved Life Safety Outcomes During Earthquake Early Response in Canada
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
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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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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