Mutation effect of acoustic and electromagnetic emissions of hard rock impact failure
Bibliographic record
Abstract
To reveal acoustic emission and electromagnetic emission effects during hard rock impact failure is a crucial issue for monitoring and warning rockburst risk induced by hard roof fracture and fall. The presented research focuses on acoustic emission and electromagnetic emission and microseismic effects detected during laboratory tests and by in situ multi-parameter observations, and the field observations agreed satisfactorily with the experimental evidences. The following main conclusions were drawn: (1) the stress level, frequency of micro-cracks, and impact failure regularity of hard rocks can be revealed with electromagnetic emission and acoustic emission/microseismic parameters, respectively; (2) acoustic emission/microseismic event counts can directly reveal the cracks change in rocks, and the initiation, propagation, and coalescence of micro-cracks can be presented as first increase, followed by decrease in acoustic emission/microseismic event counts; (3) in most cases, only when stress suddenly decreases or the rock final collapses, acoustic emissions show obviously abnormal; and (4) acoustic emission/microseismic can be more effectively applied to warn rockburst danger. The above conclusions may shed light on the effective monitoring and warning methods of rockburst triggered by hard roof fall, and events contribute to some interpretations to originally transient precursors of hard rock fracturing.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".