MétaCan
Menu
Back to cohort
Record W3004189978 · doi:10.5539/esr.v9n1p72

Three-Dimensional Analysis of Gate-Entry Stability in Multiple Seams Longwall Coal Mine Under Weak Rock Conditions

2020· article· en· W3004189978 on OpenAlexvenueno aff
Pisith Mao, Takashi Sasaoka, Hideki Shimada, Akihiro Hamanaka, Sugeng Wahyudi, Jiro Oya, Naung Naung

Bibliographic record

VenueEarth Science Research · 2020
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersJapan International Cooperation Agency
KeywordsCoal miningMining engineeringLongwall miningCoalProblems in coal miningGeologyInstabilityWork (physics)Geotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

A study of multiple seams longwall mining is proposed to investigate its applicability in Indonesia coal mine. The study area of this research is PT Gerbang Daya Mandiri (GDM) coal mine located in East Kalimantan Island. The study of seam interaction is crucial for developing multiple seams longwall mining especially when it comes to weak rock conditions which are usually found in most of the coal reserves in Indonesia. This paper will use numerical simulation to investigate the effect of the first mined-out seam on the development of the second coal seam gate-entry by considering a couple of key parameters including depth of the coal seam and interburden length. The simulation model consists of two main indicators for instability which include failure zone, the contour of safety factor. The results show that the effect of seam interaction on gate-entry has different intensity based on the thickness of the interburden and coal seam depth. This work also provided appropriate support configuration for maintaining the stability of gate-entry.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.102
GPT teacher head0.331
Teacher spread0.229 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEarth Science ResearchSame topicRock Mechanics and ModelingFrench-language works237,207