Seismic vulnerability assessment for Montreal
Bibliographic record
Abstract
In Canada, Montreal is the second city with the highest seismic risk. This is due to its relatively high seismic hazard, old infrastructures and high population density. The region is characterised by moderate seismic activity with no recent record of a major earthquake. The lack of historical strong ground motion records for the region contributes to large uncertainties in the estimation of hazards. Among the sources of uncertainty, the attenuation function is the main contributor and its effect on estimates of risks is investigated. Epistemic uncertainty was considered by obtaining damage estimates for three attenuation functions that were developed for Eastern North America. The results indicate that loss estimates are highly sensitive to the choice of the attenuation function and suggest that epistemic uncertainty should be considered both for the definition of the hazard function and in loss estimation methodologies. Seismic loss estimates are performed for a 2% in 50 years seismic threat, which corresponds to the design level earthquake in the national building code of Canada, using HAZUS-MH4 for the Montreal region over 522 census tracts. The study estimated that for the average scenario roughly 5% of the building stock would be damaged with direct economic losses evaluated at 1.4 billion dollars for such a scenario. The maximum number of casualties would result in approximately 500 people being injured or dead at a calculated time of occurrence of 2 pm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".