Strainburst hazard awareness for development miners
Bibliographic record
Abstract
In the course of new mine construction or of expanding mine workings, the mine development crews are the first to encounter the realities of stress related strain behaviour of the rock. These hazards are encountered generally before the more comprehensive seismic monitoring and rockburst management systems, common in production areas, are in place. As such, strainbursting must be considered an important hazard facing mine personnel. The nature of such hazards, although broadly foreseeable, is that their occurrence is unpredictable. Using a risk-based approach, Cementation Canada has developed practical guidelines for strainburst hazard awareness for its development miners and shaft sinkers. The objectives are to raise awareness, to highlight observable indicators, and to minimise exposure and mitigate the negative impacts of strainbursting, with the ultimate goal of zero harm. This paper covers the background to this challenge, explains how it fits into the Internal Responsibility System (IRS), and provides details of how the programme is being implemented. It is desired to share this programme to improve safety in our industry and to gather feedback in order to continuously improve upon this initiative.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".