Historical Landslide Fatalities in British Columbia, Canada: Trends and Implications for Risk Management
Bibliographic record
Abstract
According to a Canadian government database, landslides are the most common type of disaster that occurs in the province of British Columbia. Recently there has been a trend in British Columbia toward using quantitative risk assessments to estimate life-loss risk at landslide hazard sites, and to compare these estimates with risk tolerance thresholds to determine the necessity for, and extent of, risk management measures. These risk estimates are most often calibrated by so-called ‘expert judgment’ because historical landslide fatality data are not readily available. This article addresses this gap by summarizing available historical data to better inform expert judgment. It shows that fatalities caused by landslides in British Columbia are rare (approximately one fatality per year in the last decade) and have decreased with time despite rapid population growth. Approximately half of these fatalities in the last decade are related to debris flows and debris floods that impact houses, whereas the other half are related to rockfalls, debris flows, and debris floods that impact highways. A comparison with other hazard types in the Canadian government’s disaster database suggests that, while not particularly deadly, landslides are still important because of the economic damage and service disruptions they cause. Although the data are specific to British Columbia, the methods for identifying and presenting landslide risk trends could be modified and adopted in other world regions where landslide fatality data are collected and quantitative risk management methods are utilized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".