Hazus: A standardized methodology for flood risk assessment in Canada
Bibliographic record
Abstract
While Canada is exposed to a variety of natural hazards, most risk and emergency managers presently lack the necessary tools and guidance to adequately undertake rigorous risk assessments. Recently, Natural Resources Canada (NRCan) has adopted Hazus, a standardized methodology for estimating potential losses from natural hazards developed by the US Federal Emergency Management Agency (FEMA, fema.gov/hazus) as one of the best practice methods for risk assessment. Hazus estimates potential losses from earthquakes, floods and hurricanes, and includes a hazard and inventory database needed to conduct baseline risk assessment studies. An agreement has been signed with FEMA to adapt and co-develop a harmonized North American version of the Hazus methodology. At the same time, collaboration has been initiated within the federal government between the departments of Natural Resources, Environment, Defence and Public Safety to promote widespread usage of Hazus among the full range of Canadian decision-makers. This article reports the typical features of the Canadian version of the Hazus flood module and summarizes ongoing activities and potential challenges in implementing this model in Canada.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".