Managing geohazard risk during mineral exploration at remote locations in rugged terrain and tropical environments
Bibliographic record
Abstract
Mineral exploration projects in tropical environments can be exposed to a range of geohazards, including landslide, rockfall, debris flow, flooding, and subsidence. Understanding the geohazard types present, and their potential consequences at a proposed drill pad or camp site, is critical to managing the project’s geohazard risks. During the early stages of exploration, typical datasets used to map and evaluate geohazards, such as stereo airphotos and airborne LiDAR data, are often not available; as a result, engineers and geologists must rely on reconnaissance-level desktop studies and field observations of the geomorphology to estimate the risk of geohazard exposure. In order to effectively estimate geohazard risk at individual sites in a systematic manner, an evidence-based system was developed employing a standard risk equation. The components of the field-based geohazard risk assessment system include identifying the geohazard type and estimating the annual probability of occurrence at a specific location, estimating the spatial probability of the geohazard reaching the elements at risk, estimating the vulnerability of the elements at risk to the geohazard, and estimating the temporal probability that the elements at risk would be present when a geohazard occurs. This approach enables credible geohazard threats to be rated and facilitates appropriate risk management approaches suitable for each location and geohazard type. In parallel with the geohazard risk ratings, geohazard risk can be managed through more detailed assessment, awareness training, engineering measures, relocation of drill sites and infrastructure, and trigger action response plans. This paper presents a case study that employs the methodology at a greenfield exploration project site in tropical jungle in mountainous terrain.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".