A monitoring strategy to assess the effectiveness of pillar wrapping
Bibliographic record
Abstract
Pillar wrapping using cable slings is an increasingly popular ground support strategy to control large deformations in block cave mines. Although installation can be relatively costly and labour intensive, pillar wrapping is attractive as it is more effective than other ground support options. This is due to the greater elongation capacity of cable slings compared to other ground support elements. At this time, however, the performance of pillar wrapping in block cave mines is mostly anecdotal and based on limited quantified performance data. This paper presents a data-driven monitoring strategy to assess the performance of pillar wrapping at the New Afton Mine. The mine currently employs cable slings to support the junctions between the production drives and drawpoints in the B3 mining area. In a field investigation, as part of the pillar wrapping strategy, instrumented cable bolts have been installed to directly monitor the strain and loads that develop in the pillar wrapping support system during the cave initiation and propagation. The analysis of the cable instrumentation data provides for a data-driven approach for understanding the performance of the cables used for pillar wrapping. An objective of this investigation is to provide quality data to aid the mine to make informed cost-benefit decisions in its ground support decisions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".