Challenges with use of risk matrices for geohazard risk management for resource development projects
Bibliographic record
Abstract
Geohazards comprise a subgroup of natural hazards associated with geotechnical, hydrotechnical, tectonic, snow and ice, and geochemical processes that can pose a threat to worker and public safety, asset integrity, and asset management lifecycle cost. Like for most types of threats, the risks from geohazards can be assessed qualitatively or quantitatively and used to inform a geohazard management program. Most mining companies use risk matrices to aid in the assessment, prioritisation, communication and management of corporate risks. These matrices use standardised descriptions of likelihood and consequence to help users assess risks of negative outcomes to health, safety, the environment, assets, and reputation, and are tailored to each organisation’s types of risk exposure and level of risk tolerance. Geohazards and related geotechnical failures can represent low-probability, high-consequence events that plot in the highest risk zones of most corporate risk matrices. Variability in spatial and temporal probabilities for people and infrastructure exposed to geohazards can have a large influence on risk exposure, and this can be challenging to assess and communicate effectively with some risk matrices. Risk is scale-dependent: the business risk due to rockfall from a single slope along a mine access road is vastly different than the total risk due to rockfalls from all slopes along that road, yet guidance is often missing on how the risks from these scenarios should be plotted on a risk matrix. These and other pitfalls associated with use of corporate risk matrices for informed geohazard management are explored.
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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 | 0.139 | 0.348 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.028 | 0.022 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".