Rock Engineering in Canadian Mining – Highlights, Challenges and Contributions
Bibliographic record
Abstract
ABSTRACT: Recent years have been characterized by some significant world events and the continuing evolution of mining in a mature jurisdiction, such as Canada. Topics that will be discussed include managing Covid-19 and social expectations; expanding Canadian mining into harsh environments, in the Arctic and at great depth; and the need to extract more value from every drill hole in the context of rising costs and the push to reduce uncertainty. From March 2020 to the second half of 2022, the mining industry had to manage through the Covid 19 pandemic and is now navigating a period of inflationary pressures in addition to meeting social expectations in terms of inclusion and collaboration with local populations and environmental excellence. In the Canadian Arctic, the Covid-19 pandemic represented a tremendous challenge to maintain operations without putting workers and local populations in remote communities at risk. The response from both mine management and individuals was overwhelming in its seriousness, inventiveness, and teamwork. The measures that were put in place in record time ensured workers and community safety and demonstrated true leadership in Canada's most remote regions. Mining in the Canadian Arctic presents a host of challenges and opportunities which lend themselves to the development of technical solutions. For example, these include managing groundwater when mining below the permafrost, introducing the first paste backfill system operating in the Arctic and optimizing cemented rockfill in sub-zero temperatures. At the other end of the spectrum, Canadian operations are reaching unprecedented depths, mining down to 3.4 km in environments where the stress around excavations exceeds the uniaxial compressive strength of the rock. Managing seismic and rockfall risks in such environments requires robust risk management systems based on continuous monitoring and analysis, design review and research. The risk management framework for seismically active mines is presented, highlighting successes and areas of continuous improvements. Geotechnical models are the foundation of all rock engineering hazard assessment and risk management programs. Geological and structural models form the basis of any geotechnical characterization program. With rising costs and the need to reduce uncertainty there is growing pressure to extract greater value from every opportunity to look at the rock. In most cases, this means increasing the quality and the quantity of data extracted from drill cores and drill holes. Technological advances in core scanning and borehole geophysics are leveraged to enhance geotechnical databases and models.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".