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Record W4320511356 · doi:10.2991/978-2-494069-31-2_97

Development and Application of Roof Support of Roadways in Mines

2022· book-chapter· en· W4320511356 on OpenAlex

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

VenueAdvances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicGeomechanics and Mining Engineering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRoofFrame (networking)Mine safetyEngineeringProduction (economics)Order (exchange)Labor intensityCivil engineeringConstruction engineeringRisk analysis (engineering)Mining engineeringForensic engineeringCoal miningBusinessWaste managementMechanical engineeringCoalEconomics

Abstract

fetched live from OpenAlex

Underground mining is always the most dangerous industry.The mines' inside support structures are always the most critical part of studying in order to increase safety.The inside roof support has three main categories: frame stand support, anchor rock bolt support and reserved column support.Each method has its own advantages, but the disadvantages are also inescapable; thus, using a single design would heavily impact field practice, safety measurements and economic benefits.Therefore, under the situation that current methods can hardly have a technical breakthrough, it is very necessary to have more profound studies to combine two or more methods organically to adopt each's good points and avoid shortcomings.In the end, it would increase the underground support results and safety.Also, it can save massive initial costs and further maintenance fees, reduce labour intensity, improve the working environment and increase production efficiency.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0020.006
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.061
GPT teacher head0.388
Teacher spread0.327 · 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