Experimental study on multi-parameter performance differences of coal with different rockburst tendencies
Bibliographic record
Abstract
Addressing the difficult issue of precursor damage warning signs for the destruction of coal with different rockburst tendency, this paper conducted research on the multi-parameter evolution laws by acoustic-thermal-energy synchronization experiments. The results show that as rockburst tendency increases, uniaxial compressive strength (UCS), elastic strain energy density, energy ringing count, cumulative energy counts, and acoustic emission (AE) impact intensity all increase significantly; the post-peak stress–strain curve is gradually steeper and the impact damage is more severe than that because the denser structure accumulates more elastic energy, causing severe impact damage; moreover, the damage form gradually changes from shear to split damage, triggering the infrared radiation temperature changing from warming to cooling type. Disaster precursor signatures appear in each covariate, respectively, and a calm zone appears in the energy ringing counts of coal specimens with weak and strong rockburst tendency; as rockburst tendency increases, the b-value precursor signature points fall behind, being 87%, 95.3%, and 97.9% of the UCS, respectively; correspondingly, a calm period appears in the infrared radiant temperature, but lags behind the signals of AE and the UCS. There are differences in homologous monitoring information, mainly due to the different densification, strength, and damage form. This paper lays a foundation for rockburst prevention and control.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".