NATIONAL SEISMIC HAZARD ASSESSMENTS IN CANADA: APPLICATIONS, PROGRESS AND FUTURE CHALLENGES
Bibliographic record
Abstract
National seismic hazard models form the foundation of earthquake risk reduction strategies to minimize human casualties and economic losses from future earthquakes. Natural Resources Canada and its predecessors have been generating national estimates of seismic hazard in Canada for over 70 years. These national models have formed the basis of seismic design values for the National Building Code of Canada since 1953. As knowledge of seismicity in Canada has grown, and as probabilistic seismic hazard analyses have become more sophisticated, Canada's national mapping efforts have become increasingly more complex. Currently, the 6th Generation Seismic Hazard Model of Canada is the most advanced national assessment to date. The increased need to transition from quantitative hazard-based decisions to risk-based decision making has seen Canada's national seismic model incorporated into probabilistic national earthquake risk models. This paper will describe current challenges in estimating and modelling seismic hazard in Canada with a particular focus on 1) how models need to be adapted for suitability into either building code or national risk models and 2) updates on model improvements expected for future generations of models.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".