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Record W2099113973 · doi:10.5539/mas.v3n8p95

Study on the Technology of the Coal Mining Safety Monitoring System

2009· article· en· W2099113973 on OpenAlexvenueno aff
Zhi Chang, Zhangeng Sun, Junbao Gu

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldComputer Science
TopicTechnology and Security Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCoal miningRadio-frequency identificationFlexibility (engineering)HarmComputer scienceALARMWirelessSafety monitoringComputer securityProduction (economics)Work (physics)Control (management)VisualizationRisk analysis (engineering)TelecommunicationsBusinessCoalEngineeringData mining

Abstract

fetched live from OpenAlex

New development and application of the wireless radio frequency make the application of the RFID (Radio Frequency Identification) technology become more and more extensively, and combining the characteristics of RFID technology with the mine, we established an underground safety monitoring system with integrality, real-time and flexibility, which could automatically alarm when dangers happen and enhance the search and rescue efficiency. The system could manage the harm-gas concentration, planning arrangement of workers, access control for the works passing in and out laneway, distribution of laneway personnel and workers’ data, realize the informationization and visualization of the underground management, and enhance the mining production management level and the work safety level of the mine.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.242
Teacher spread0.222 · 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 designBench or experimental
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

Citations3
Published2009
Admission routes1
Has abstractyes

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