Flooding: Contributing factors to residential flood damage in Canada
Bibliographic record
Abstract
Flood risk management must rely on the best estimate of potential damages to make oriented decisions. Flood depth damage curves are the most used method of estimating direct property damage. Although widespread, this method involves high uncertainty, as limited factors are typically considered. This paper aims to identify and prioritize additional contributing factors to flood damage that should be considered in damage estimation to reduce uncertainty. Forty-five Canadian experts, including adjusters, engineers, estimators, and contractors, identified and prioritized 40 factors contributing to flood damage in residential buildings beyond the traditional inundation depth factor. Analysis reveals that municipalities play a significant role, as seven of the ten most important factors fall under their responsibility in collaboration with provincial authorities who establish overarching regulations and policies. Shared responsibility encompasses key factors such as the distance of a building from a water course as part of land use planning, obligatory compliance with new building codes, and the design and maintenance of critical infrastructure like sewer systems. Given these extensive responsibilities, Canadian municipalities have a crucial role in proactively reducing flood risk and mitigating the impact of flood events. Expert judgment on the prioritization of factors reinforces the need to integrate a broader range of physical vulnerability and exposure factors into flood risk estimation tools and should encourage municipalities to collect and optimize the use of these factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".