Bibliographic record
Abstract
The requirement for resources has resulted in mining activities moving into more challenging conditions, from conventional, gravity driven ground conditions to highly stressed rock mass. In highly stressed, burst-prone rock masses, mining-induced seismicity presents a challenge to most ground support systems. The capacity of conventional rock reinforcement elements such as grouted rebar rockbolts and friction rockbolts is often found to be inadequate when subjected to large deformations resulting from mining-induced seismicity. The requirement to sustain large loads over large deformations has led to the development of several energyabsorbing rock reinforcement elements. The performance of an energy-absorbing element is typically determined through a laboratory-based drop test. During a laboratory-based test, the kinetic energy of a known mass, released from a known height, is transferred to the rock reinforcement element installed in a steel tube. There are two primary drop test methods, impact testing and momentum transfer. Although there are arguably differences between the two methods, both share common limitations. This paper provides a summary of recent investigations conducted to understand the effect of the test parameters on the performance of rock reinforcement elements determined through laboratory-based drop testing. The purpose is to provide a high-level overview rather a detailed review.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".