Bibliographic record
Abstract
Seismic loss estimation for Montreal, Canada is performed for a 2% in 50 years seismic hazard using the HAZUS-MH4 tool developed by US Federal Emergency Management. The software is manipulated to accept a Canadian setting for the Montreal study region, which includes 522 census tracts. The accuracy of loss estimations using HAZUS is dependent on the quality and quantity of data collection and preparation. The data collected for Montreal study region comprise: 1) the building inventory 2) hazard maps regarding soil amplification, liquefaction, and landslides 3) population distribution at three different times of the day 4) census demographic information and 5) synthetic ground motion contour maps using three different ground motion prediction equations. All these data are prepared and assembled into geodatabases that are compatible with the HAZUS software. The study estimated that roughly 5% of the building stock would be damaged with direct economic losses evaluated at 1.4 billion dollars for a scenario corresponding to the 2% in 50 years scenario. The maximum number of casualties associated with this scenario corresponds to a time of occurrence of 2pm and would result in approximately 500 people being injured. 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 suggests that epistemic uncertainty should be considered both for the definition of the hazard function and in loss estimation methodologies. The next steps in the study should be to increase the size of the survey area to the Greater Montreal which includes more than 3 million inhabitants and to perform more targeted studies for critical areas such as downtown Montreal, and the south-eastern tip of Montreal. The current study was performed mainly for the built environment; the next phase will need to include more information relative to lifelines and their impact on risks.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".