Introducing uncertainty in hazard analysis in a simple way: example of rockfalls
Bibliographic record
Abstract
One of the main problems of risk assessment is to evaluate the uncertainty of the results. One relevant solution is to provide exceedance curves based on simulations of the risk calculation (Macciotta et al., 2016; Jaboyedoff et al. 2021), as can be done with CAT models. Instead of performing a single calculation, up to 106 are performed with imputed viability based on different approaches such as observed distributions, standard probabilistic laws such as Poisson or uniform distribution, expert knowledge based on triangular distributions, etc. This can be done on the basis of a "deterministic calculation" of the risk, which allows a better assessment of the uncertainty of the risk.Drawing upon a precedent risk calculation study within a road corridor, a novel risk calculation methodology is suggested, employing stochastic simulations to introduce variability across the parameters in the risk equation. The outcome manifests as an exceedance curve akin to those generated by catastrophe models. This approach systematically introduces uncertainty into the risk calculation, providing a simplistic means to address inadequately documented cases with limited data. This approach tends to minimise risk or call risk calculations into question. References:Jaboyedoff, M., Choanji, T., Derron, M.-H., Fei, L., Gutierrez, A., Loiotine, L., Noel, F., Sun, C., Wyser, E. & Wolff, C. 2021. Introducing Uncertainty in Risk Calculation along Roads Using a Simple Stochastic Approach. Geosciences, 11, doi: 10.3390/geosciences11030143.Macciotta, R., Martin, C.D., Morgenstern, N.R. & Cruden, D.M. 2016. Quantitative risk assessment of slope hazards along a section of railway in the Canadian Cordillera—a methodology considering the uncertainty in the results. Landslides, 13, 115-127, doi: 10.1007/s10346-014-0551-4.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".