Bibliographic record
Abstract
Open pit slopes within complex structural geology are challenging to evaluate as complexity increases the potential for unidentified hazards. As such, geotechnical assessments require systematic methodologies to identify and prioritise areas for detailed stability analysis or future risk mitigation. For the Burnt Ridge North (BRN) pit at Line Creek Operations (LCO), a metallurgical coal mine in the Rocky Mountains of British Columbia, Canada, slope stability is often a function of complex geological structure. In recognition of the increased potential for unidentified failure mechanisms, a geometrical risk assessment methodology was developed and conducted as a screening tool to prioritise additional geotechnical assessments and mitigation planning. Using a detailed 3D structural model and a system of parametric conditions related to likelihood and consequence, the pit shell was analysed to identify and prioritise areas of geotechnical risk. The safety map feature in Slide3 (Rocscience 2024) was then used to compare and validate the methodology. The geometric review framework provides a rapid and cost-effective means to highlight critical zones in complex geotechnical environments. The framework needs to be tailored to site-specific conditions. Results can be used to guide risk management efforts and engineering efforts toward more detailed numerical analyses and field investigations. The Slide3 model proved to be a useful tool for comparison, but it does not consider the consequence of instability on operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".