A new paradigm in ground support monitoring through ultrasonic monitoring of clusters of rockbolts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In most of today’s underground mines, ground support monitoring is mainly conducted through using microseismic sensors, LiDAR, extensometers, cameras, or visual inspection. These monitoring tools are complementary in nature. Due to high costs associated with purchase, installation, maintenance and utilisation, they are usually deployed or used at sparsely selected critical locations, some of them on a noncontinuous basis. This means that some important pieces of information on ground support conditions may be missing either location-wise or time-wise. In the last four years, the Energy, Mining and Environment Research Centre of the National Research Council Canada (NRC), in collaboration with CanmetMINING of Natural Resources Canada (NRCan), has developed next generation ultrasound rockbolt sensors (RBSTM) for monitoring load change and deformation experienced by rockbolts. Intrinsically low costing and installation onto exposed end of rockbolts using production bolters, the technology is meant to be deployed on a large number of rockbolts whereby the instrumented rockbolts become a network of ground condition sensors to provide on-demand 3D mapping of ground stress change and deformation all over excavated zones. Field trial data collected in a production mine has demonstrated that monitoring a cluster of rockbolts can provide much more meaningful and reliable information about ground condition when compared with information provided by a single instrumented rockbolt. Therefore, monitoring clusters of rockbolts is recommended as being an effective practice for ground support monitoring.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it