Bibliographic record
Abstract
Although earthquakes have been recognised as major natural hazards with the potential to cause loss of life, property damage, and social and economic disruption in Canada, most risk and emergency managers still lack the necessary tools and guidance to adequately undertake rigorous risk assessments. Recently, Natural Resources Canada (NRCan) has adopted Hazus, a standardized best-practice methodology developed by the US Federal Emergency Management Agency (FEMA) for estimating potential losses from common natural hazards, such as earthquakes, floods, and hurricanes. Hazus combines science, engineering knowledge, and mathematical modelling with geographic information systems technology to estimate physical damage and economic and social losses. Besides the ground shaking, the earthquake model considers landslide, liquefaction, and fault rupture susceptibilities. Depending on the severity of the resulting transient ground motion and permanent ground deformation, five potential damage states (none, slight, moderate, extensive, complete) are employed to estimate the amount of structural damage and consequent economic and social losses. This note reports some of the typical features of the recently adapted Hazus earthquake model, with an emphasis on the considerations of earthquake-induced hazards, and overviews the ongoing activities and potential challenges in implementing this model in Canada.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".