Extra-Tropical and Tropical Cyclone Vulnerability in Canada: Two Comprehensive Models
Bibliographic record
Abstract
Natural hazards, particularly extra-tropical (winter storms) and tropical cyclones, could have devastating impacts on human life, structures, the environment, and the economy. It has not been long enough to forget the destructive impacts of the 1998 ice storm and, more recently, the costly impact of Hurricane Irene in 2011. In terms of today's economy, the 1998 ice storm would have caused insured and economic losses of about 3 and 6 billion Canadian dollars (CAD), respectively. More recently, Hurricane Irene left an impact across Ontario, Quebec, and New Brunswick, causing approximately 200 million CAD in damage to insured properties. To estimate possible damage and losses within Canada due to extra-tropical cyclones, a comprehensive probabilistic model was developed which incorporates the meteorological hazards (wind, snow, freezing temperatures, and ice accumulation) and the associated vulnerability of Canadian built structures. In addition, a methodology for accounting for the temporal and regional vulnerability of structures was also implemented, reflecting changes in the wind and snow load design methodologies as well as the construction practices in Canada. A similar approach is used to develop a model for assessing the tropical cyclone risk 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".