Slope risk management in light of uncertainty and environmental variability—2021 Canadian Geotechnical Colloquium
Bibliographic record
Abstract
Landslides are common across Canada and they pose hazards to human safety, economic activities, and the environment. Robust risk management strategies are necessary for sustainable development. A slope risk management framework has been adopted by the geotechnical community for approximately four decades allowing a systematic, consistent and transparent framework for managing risks. Implementing this framework is associated with uncertainties embedded in our estimates of risk. This paper presents a brief summary of the sources and categories of uncertainty in geotechnical slope engineering and focuses on two topics: (1) estimates of uncertainty in risk calculations and (2) temporal changes in landslide likelihood as a function of weather and steps towards estimating landslide risk changes with climate change. The paper argues that a quantitative risk assessment should not focus on the final risk calculation, but the overall knowledge gained. This allows comprehensive documentation of sources of uncertainty and how they impact geotechnical and risk assessments. Furthermore, the paper outlines approaches to define quantitative correlations between rock fall occurrences and weather, which can be leveraged to estimate changes in rock fall risk with climate change. The paper corresponds to, and expands on, the 2021 Canadian Geotechnical Colloquium.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".