Review of quality assurance methods for hydro-powered resin bolting
Bibliographic record
Abstract
Resin bolting in South Africa has been developed and refined in underground coal mines (i.e., soft rock) since the late 1940s. The extension of resin bolting to hard rock mines started in 2003 and has required significant adaptations, challenging the status quo from both a supplier and applicator perspective. As far as resin bolting is concerned, the most affected aspect in coal mining and hard rock mining environments, is the rock drills or machinery used for installation. Hard rock mines mostly use hand-held airlegs with limited thrust, low torque, and rotation speed, relative to coal mine mechanised bolters. Resin bolt development for hard rock mines was, and still is today, predominantly focussed on compensating for the 'shortfalls' of using hand-held airlegs. At face value, hydro-powered rock drills, which are more powerful than traditional handheld equipment, provided hard rock mines with a means to attain more consistent and reliable resin bolt installations. This paper assesses the quality and performance of hydro-powered resin bolting at an intermediate to deep level platinum mine in South Africa and its potential contribution to rock-related instabilities when applied erroneously. The findings of this paper necessitate a revision of current quality control and quality assurance measures related to resin bolting for underground support.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".