Bibliographic record
Abstract
One of the most common methods that are practiced today for controlling violent rock failures in underground mines is destress blasting. One aspect of this method involves drilling and blasting areas that are stiff and highly stressed such as pillars, mining fronts and shaft sinking floors, to help dissipate high stress and energy accumulation, thus rendering a safer mining environment. Another aspect of the method relies on large scale blasting of one or more slots or panels near the active mining area to create a stress shadow around it and help reduce the stress and energy concentration. While the merits of the destress blasting method are conceptually well appreciated by many mines, its efficient implementation in the field has been hampered by the diversity of available information, the scarcity of well-documented destressing programs, as well as the absence of a dedicated design/analysis method. This has made the destress blasting method more like an art than an engineering science. This paper reviews the background theory, benefits, constitutive modelling, and practice of destress blasting. Current research on the evaluation of the destress blasting efficiency is discussed, and future research directions are highlighted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".