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Reliable Communication Network for Emergency Response and Disaster Management in Underground Mines

2017· book-chapter· en· W2649696324 on OpenAlexaffabout
S. M. Kamruzzaman, Xavier Fernando, Muhammad Jaseemuddin, Wisam Farjow

Bibliographic record

VenueAdvances in public policy and administration (APPA) book series · 2017
Typebook-chapter
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmergency managementEngineeringCommunications systemMine safetyComputer securityEmergency responseTelecommunications networkConstruction engineeringRisk analysis (engineering)Computer scienceTelecommunicationsBusinessCoal miningWaste managementMedical emergency

Abstract

fetched live from OpenAlex

Emergency response and disaster management in underground mines are very challenging due to the hostile nature. Environment monitoring in mines has been an obligatory requirement to ensure safe working conditions for miners. Reliable communication network is essential to quickly detect the underground condition especially in emergency situation and to conduct proper rescue operations. This chapter presents an overview of reliable communication network needed for emergency response and disaster management in underground mines. The chapter begins by introducing the most common accidents occurring in the mining, underground mine environment and channel properties. Subsequently, communications in underground mines, existing underground communication and tracking systems, and disaster forecasting & mine safety management are discussed. The chapter also covers post-disaster mine communications & tracking systems and optimized backbone networks for underground mines. Finally, the chapter concludes by reporting relevant research at Ryerson Communications Lab and pointing out some open issues and possible research directions.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.000
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 head0.022
GPT teacher head0.295
Teacher spread0.272 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations8
Published2017
Admission routes2
Has abstractyes

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