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Record W2153084179

The Coal Mine Roof Rating in Mining Engineering Practice

2003· article· en· W2153084179 on OpenAlexaboutno aff
Christopher Mark, G.M. Molinda

Bibliographic record

VenueResearch Online (University of Wollongong) · 2003
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsRock mass classificationMining engineeringRoofCoal miningDrillGeologyEngineeringRock mechanicsCoalGeotechnical engineeringCivil engineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.283
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2003
Admission routes1
Has abstractyes

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