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Record W2017902463 · doi:10.1139/t10-009

Quantitative prop support estimation and remote monitor early warning for hard roof weighting at the Muchengjian Mine in China

2010· article· en· W2017902463 on OpenAlexvenueno aff
Yunliang Tan, Tongbin Zhao, Ya-Xun Xiao

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeoscience and Mining Technology
Canadian institutionsnot available
FundersTai'shan Scholar Engineering Construction Fund of Shandong Province of China
KeywordsRoofCoal miningBeddingGeotechnical engineeringExcavationMining engineeringWeightingWarning systemSpan (engineering)BedGeologyCoalEngineeringEnvironmental scienceCivil engineeringWaste management

Abstract

fetched live from OpenAlex

The complex coal seam structure and hard roof at the Muchengjian Mine were studied, and the equivalent tensile strength of the roof was determined by a retrospective analysis of similar roof cave-ins. The prop spacing or number of hydraulic props required per unit area were obtained by analyzing the roof caving span and thickness. The early warning threshold bedding vertical separation velocity for hard roof caving at the Muchengjian Coal Mine was determined to be about 14 mm/day, and the newly invented “bedding separation remote monitoring system” (BSRMS) was used for the first time for early warning of a roof fall. A total of 48 trials of early warning roof weighting were performed at the Muchengjian Mine. It was found that not only were all the early warnings accurate, but the support system was also safe and reliable.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2010
Admission routes1
Has abstractyes

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