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Record W2139899388 · doi:10.1504/ijhfms.2010.040274

Using visibility tools in Classic JACK to assess line-of-sight issues associated with the operation of mobile equipment

2010· article· en· W2139899388 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

VenueInternational Journal of Human Factors Modelling and Simulation · 2010
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsLaurentian University
Fundersnot available
KeywordsVisibilityOperator (biology)SightMobile deviceComputer scienceLine (geometry)Non-line-of-sight propagationEngineeringSimulationTelecommunicationsWirelessOperating system

Abstract

fetched live from OpenAlex

Several methods exist for measuring line-of-sight (LOS) associated with the operation of operator controlled mobile equipment but there is no consistent criteria used for all machine types or operating scenarios. Moreover, the standards that do exist are not particularly applicable for carrying out LOS analysis on mobile mining equipment. This paper describes a new method to evaluate LOS associated with the operation of mobile equipment, the LOS boxplot. The human simulation program, Classic JACK was used to analyse LOS from the operating position of five different load-haul-dump (LHD) vehicles, typically used in underground hardrock mining. The LOS boxplot method was able to demonstrate LOS associated with five LHD vehicles, and it was able to illustrate LOS improvements associated with the design modifications tested. The examples provided also show the applicability of the method for evaluating operator LOS from other types of heavy equipment.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.222

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 head0.088
GPT teacher head0.343
Teacher spread0.255 · 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