Outreach and engagement to ensure the success of an Earthquake Early Warning System for Canada
Bibliographic record
Abstract
<!--!introduction!--> Natural Resources Canada (NRCan) is developing a national Earthquake Early Warning (EEW) system for Canada. The network will focus on regions with a) an expectation of strong earthquakes, and b) concentrations of population and/or critical infrastructure (CI). These regions include parts of British Columbia, Ontario and Quebec. The system will facilitate mitigation of earthquake impacts, allowing for timely and appropriate response actions by the public, emergency measures organizations, CI operators, and other industrial facilities. However, for the system to be effective, a culture of awareness is necessary to ensure appropriate protective actions are taken when alerts are received. A coordinated public education campaign is underway to help achieve this. NRCan is hosting workshops and other outreach activities with CI operators to ensure they are aware of the benefits of installing systems that automatically translate EEW alerts into protective actions. Simultaneously, NRCan is encouraging equipment providers in Canada to develop such automated systems. In these efforts, NRCan is collaborating with federal and provincial public safety organizations, private and international partners, and Non-Governmental Organizations. This will ensure that EEW messaging is authoritative, consistent and accessible. Social science research by collaborators is underway and will guide the education of vulnerable populations including First Nations peoples, new immigrants, people with low income, and the elderly. By making it possible to take safe actions before the arrival of potentially harmful shaking, the national EEW system will contribute to the reduction of earthquake risk in Canada.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".