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Record W2469939668 · doi:10.17580/gzh.2016.05.11

International experience of studies into integrated coal mining with underground method

2016· article· en· W2469939668 on OpenAlexaboutno aff
С.А. Воробьев, N. M. Kachurin

Bibliographic record

VenueGornyi Zhurnal · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsCoal miningCoalMining engineeringEnvironmental scienceGeologyEngineeringWaste management

Abstract

fetched live from OpenAlex

The article offers a review of international applied researches in the field of underground coal mining for the last ten years. The majority of the research is implemented in the countries with the traditionally developed coal mining industry—USA, Australia, Canada and Poland. The scope of the review embraces trends that are connected with the coal mining constraints, on the other hand, and precondition development of the alternative heat and electricity generation, on the other. For instance, the use of coalbed methane, gasification of coal, prevention of spontaneous fires and coal and gas outbursts, etc. The ecological aspect is involved in the form of mitigation of the environmental impact induced by mining, starting from the launch of a mine and finishing with the mine closure and complete utilization of waste accumulated in this fuel and energy sector of economy. The most important factor in coal mining is coalbed methane. A lot of international studies have focused on methane generation, removal and benefi cial use. The working knowledge on using methanebearing mine air taken from methane drainage systems in the capacity of an energy-carrier is available. The authors substantiate the conclusion that modern technologies of underground coal mining are extremely knowledge-intensive; for this reason, any production or mine requires technical and scientific support to be efficient and safe. Development of most efficient and safest technologies of coal mining and preparation in Russia under conditions of unsteady economic conditions requires analyzing foreign research fi ndings, selecting the best approaches and screening out admittedly poor results.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.037
GPT teacher head0.313
Teacher spread0.276 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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