The Coal Mine Roof Rating in Mining Engineering Practice
Bibliographic record
Abstract
ABSTRACT: The Coal Mine Roof Rating (CMRR) system was developed ten years ago to fill the gap between geologic characterization and engineering design. It combines many years of geologic studies in underground coal mines and worldwide experience with rock mass classification systems. Like other classification systems, the CMRR begins with the premise that the structural competence of mine roof rock is determined primarily by the discontinuities that weaken the rock fabric. Since its introduction, CMRR has been incorporated into many aspects of mine planning, including longwall pillar design, roof support selection, feasibility studies, and extended cut evaluation. It has also become truly international, with involvement in mine designs and funded research projects in South Africa, Canada, and Australia. Most recently, a new streamlined process to determine the CMRR from exploratory drill cores has been developed. Just three types of information are now required: • Fracture spacing Rock Quality Designation (RQD) from a standard geotechnical drill log • Uniaxial compressive strength from standard lab tests, geophysical downhole logging, or axial point load tests, and; • Diametral point load testing. The CMRR has been implemented in a computer program, which can be obtained from NIOSH free of charge. The program facilitates calculation of the CMRR from either underground or drillcore data. Values from many
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".