A flood risk assessment for the City of Chilliwack on the Fraser River, British Columbia, Canada
Bibliographic record
Abstract
Flood hazard management in Canada is predicated on an event of arbitrary return period – the ‘design flood’, which on the Fraser River is the flood of record, with an estimated return period of 500 years. Recent studies have demonstrated that the existing dikes will not contain design flood levels, in a location where the potential consequences of flooding have increased exponentially during the past century. This suggests that a hazard-based approach is questionable for effective flood management. A pilot flood risk assessment was completed for the City of Chilliwack, 80 km east of Vancouver. We assessed direct and indirect economic losses for three hypothetical dike breach scenarios for flood return periods from 100 to 1000 years. We estimate that total losses would exceed CND $1 billion (€720 million) for the flood scenarios examined, not including intangibles such as emotional and cultural losses, environmental contamination, or changes in the quality and abundance of aquatic and terrestrial habitat. The study concludes with recommendations that would lead to a risk-based decision-making framework for flood mitigation design and emergency preparedness.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".