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Record W2575105247 · doi:10.5555/3042094.3042506

Simulation-based analysis of operational efficiency and safety in a virtual environment

2016· article· en· W2575105247 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

VenueWinter Simulation Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsWaiward Steel (Canada)University of Alberta
Fundersnot available
KeywordsProductivityComputer scienceDiscrete event simulationWork (physics)Human factors and ergonomicsProduction (economics)Risk analysis (engineering)EngineeringSimulationPoison control

Abstract

fetched live from OpenAlex

Effective evaluation of the productivity and safety of manual operations is essential for successful planning of operations as well as for workplace design. However, actions employed by production planners to improve productivity might adversely impact the ergonomic safety of workers. To address this issue, methods and tools are required that enable simultaneous evaluation of the efficiency and safety of operations. Thus, this study proposes an approach that integrates predetermined motion time systems and ergonomic assessment into a discrete-event simulation environment, and uses inputs obtained from point cloud and 3D models of a workplace to analyze both the productivity and ergonomic safety of manual operations. The proposed approach facilitates the evaluation and improvement of efficiency and ergonomic safety of manual tasks by automating the analysis and eliminating the need for onsite measurements and observations, all without the need for extensive prior knowledge regarding how PMTSs and ergonomic assessment methods work.

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: none
Teacher disagreement score0.816
Threshold uncertainty score0.575

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)0.0010.000

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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