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Record W3127850314 · doi:10.17580/em.2020.02.06

Classification of protective pillars toward higher safety and innovation in room-and-pillar coal mining

2020· article· en· W3127850314 on OpenAlex
С. А. Прокопенко, В.В. Семенцов, M. S. Dobrovolsky, E. V. Nifanov

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEurasian Mining · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsGeomechanica (Canada)
FundersTomsk Polytechnic University
KeywordsPillarCoalCoal miningMining engineeringHard coalProduction (economics)Process (computing)EngineeringWaste managementComputer scienceStructural engineeringEconomics

Abstract

fetched live from OpenAlex

During room-and-pillar mining, protective pillars of coal are usually left standing. The protective pillars differ in shape, size, process properties and extractability ratios of coal. The authors propose a classification of the protective pillars with respect to their structure and shape in order to estimate and predict their properties. The extractability ratios of coal are calculated for the initial rib pillars and panels at different final shapes of protective pillars. The protective pillars are ranked with respects to the coal extractability ratios. Such systematization of pillars enables selecting the best engineering solutions and innovative improvement of room-and-pillar mining in specific geological conditions toward enhanced safety and efficiency of underground coal production. The article is prepared in the framework of the Competitiveness Enhancement Program of the Tomsk Polytechnic University.

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.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.026
GPT teacher head0.223
Teacher spread0.196 · 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