Landslide life-loss risk quantification based on historical fatalities
Bibliographic record
Abstract
Life-loss risk estimates inform decisions that have dramatic impacts on individuals and communities in landslide hazard zones. Literature provides methods for making these estimates through multiplication of site-specific parameters. However, there is no method in the literature for calibrating those estimates, and regional context is needed to make efficient, fair, and affordable decisions. Therefore, we developed methods to quantify risk to individuals and groups based on regional historical fatality and hazard zone population data. The methods can be used to evaluate the accuracy of large sets of site-specific risk estimates, to assign quantitative risk values to buildings and hazard zones at a regional scale, to develop a long-term regional budget for landslide risk management, or to inform selection of a risk tolerance threshold that can inform where limited resources are invested in landslide mitigation. An example of the methods is provided for a case study of residential landslide risk in British Columbia, Canada. It estimates that there are between a few hundred and a few thousand people living with individual risk exceeding 100 micromorts (1 in 10 000) per year, and between 70 and 370 landslide hazard zones with annual probable life loss greater than 1E–3 (1 death in 1000 years), based on a historical fatality rate of 0.4 to 1.4 deaths per year across the entire province.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".