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Record W1978461464 · doi:10.1504/ijhfms.2010.036790

Classic JACK modelling of driver posture and line-of-sight for operators of lift-trucks

2010· article· en· W1978461464 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
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsLaurentian University
Fundersnot available
KeywordsAnimationLift (data mining)TruckSightSoftwareComputer scienceSimulationComputer animationVirtual actorEngineeringAeronauticsVirtual realityComputer graphics (images)Automotive engineeringHuman–computer interaction

Abstract

fetched live from OpenAlex

Several reports indicate that lift-truck (LT) drivers may be at higher risk for developing musculoskeletal injuries due to postures that must be adopted in order to manoeuvre the LT in industrial workplaces. This research uses a human simulation program to quantify changes to line-of-sight (LOS) using driving simulations acquired from actual drivers in a closed arena setting. Video files acquired during the mock-up were decimated to 3 Hz and the resulting video files were coded in 3D Match. The resulting posture file was used to drive the animation tool in Classic JACK v4.1 human simulation software while quantifying LOS with additional virtual tools. On average, the drivers adopted awkward postures with increased levels of compression in order to have less overall LOS on both LT models. The method introduced was deemed a success at simulating human motion in a virtual environment from a simple, video record of the original motion.

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 score0.108
Threshold uncertainty score0.383

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 head0.043
GPT teacher head0.332
Teacher spread0.290 · 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