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Record W2100780771 · doi:10.5539/mas.v8n5p179

Justification for Remote Control of Construction and Road-Making Machines

2014· article· en· W2100780771 on OpenAlexvenueno aff
N. S. Sevryugina, Eugene Alexandrovich Volkov, Eugene Pavlovich Litovchenko

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsExcavatorVisibilityProcess (computing)Computer scienceRemote controlControl (management)Web crawlerQuality (philosophy)Computer securityRisk analysis (engineering)EngineeringCivil engineeringBusinessArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

It is evident from the experience of operating the construction machinery (excavators, bulldozers, loaders etc.) that quite often the machinery and its operators are working under severe conditions. Working under threat of rock fall, on unstable or contaminated grounds, or debris handling tends to increase the impact of adverse occupational environment on the operators’ health. When the operators are at risk, it is advisable to deploy remote process control technologies on the work sites. This is what brings research and development of modern remote control systems to the top of the chart in order to improve the productivity of machinery, enhance the safety and quality of the jobs carried out. The use of remote control will exclude the adverse impact of aggressive environment during the process operations. The research allowed to establish that the design specifics of the construction and road-making machines enable them to go far beyond the standard process layout giving into a much wider range of application, up to aggressive environments. A crawler excavator model was devised, imitating the visibility range of the process areas for the manual and remote control systems. Implementation of remote control for construction and road-making machines may become a step towards a completely new level of interaction within the man-machine-environment system.

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)0.000
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: none
Teacher disagreement score0.944
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations8
Published2014
Admission routes1
Has abstractyes

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