Hazus: A standardized methodology for flood risk assessment in Canada
Why this work is in the frame
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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.Bien que le Canada soit expos une varit de risques naturels, la plupart des gestionnaires de risques et d'urgence n'ont actuellement pas les outils ncessaires et les conseils pour bien entreprendre des valuations rigoureuses des risques.Rcemment, Ressources naturelles Canada (RNCan) a adopt Hazus, une mthodologie standardise pour estimer les pertes potentielles lis aux alas naturels mis au point par la US Federal Emergency Management Agency (FEMA,fema.gov/Hazus),comme l'une des meilleures mthodes de pratiques pour l'valuation du risques.Hazus estime les pertes lies aux tremblements de terre, aux inondations et aux ouragans, et comprend une base de donnes inventoriant les alas essentiels ncessaires pour mener des tudes prliminaires d'valuation du risque.Un accord a t sign avec la FEMA pour adapter et pour co-dvelopper une version harmonise nord-amricaine de la mthodologie Hazus.En mme temps, la collaboration a t initie au sein du gouvernement fdral entre les ministres des Ressources naturelles, de l'Environnement, de la Dfense et de la Scurit publique pour promouvoir l'utilisation gnralise de Hazus parmi l'ensemble des dcideurs canadiens.Cet article prsente les caractristiques typiques de la
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it