Quantitative risk assessment of rock slope instabilities that threaten a highway near Canmore, Alberta, Canada: managing risk calculation uncertainty in practice
Bibliographic record
Abstract
We present a quantitative risk assessment (QRA) to guide decision-making for selection of rock fall protection strategies. The analysis corresponds to a section of highway near Canmore, Alberta, Canada; where rock falls are common. Environmental concerns, tourism, and economic activities overlap the project area, which increased the complexity of the decision-making process. QRA was adopted to improve highway user safety and minimize effects on natural, social, and economic environments. Uncertainty was associated with hazard and consequence quantification, and the study elicited plausible ranges of input variables for risk calculation. Expected and range in risk were calculated for current conditions and after mitigation. Individual risk to highway users was found to be low, following the limited exposure of any particular individual. Current total risk was calculated at 2.9 × 10−4 probability of fatality and a plausible range between 2.0 × 10−5 and 5.5 × 10−3. The slope protection configuration selected had a residual total risk between 9.0 × 10−4 and 2.9 × 10−6, and a best estimate of 4.5 × 10−5. The risk levels were evaluated against criteria previously used in Canada and were considered an appropriate balance between project costs, public safety, environmental concerns, tourism, and economic activities after mitigation.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".