Could Montreal residential buildings suffer important losses in case of major earthquakes
Bibliographic record
Abstract
Recent insurance reports on seismic risk in Quebec suggest the potential for substantial losses in case of a major earthquake.A detailed analysis is performed in Montreal for residential buildings using HazCan, the Canadian version of HazUS.An inventory of population and existing building stock is developed at the scale of census dissemination areas.Wood frame structures constitute 80% of the total square footage, while unreinforced masonry buildings account for 18%.Single-family houses represent more than 36% of the square footage, followed by duplex (23%), triplex (13%) and multi-storey buildings (28%).The total value of the residential building stock is estimated around 87 billion CAD excluding contents.Six different seismic scenarios are considered which account for potential rupture sources identified through disaggregation of the seismic hazard curve and from the analysis of recent seismicity.Ground motion prediction equations for Eastern North America (CEUS, 2008) are used taking into account microzonation data in terms of Vs 30 derived soil classes.Depending on the scenario, property damage ranges from 25 to 60% of the total building stock and from 2 to 12% for severely damaged and collapsed buildings.Non-structural damage accounts for 80% of the total losses.Generally, wood frame structures perform best while masonry houses built before the 20th century account for most of the damage.The total losses vary between 1 and 12% of the value of the portfolio of residential houses (2016 value) depending on the selected scenario.Preliminary estimates of the amount of debris generated by scenario earthquakes range between 0.6 to 6 million tons, with brick and wood debris representing approximately 60% of the total.The analysis was conducted in collaboration with the city of Montreal (Direction de la sécurité civile de Montréal) and the provincial government of Quebec (Ministère de la Sécurité publique) to identify high risk areas and to improve seismic preparedness and emergency planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".