The Use of Virtual Human Factors Tools in Industry – A Workshop Investigation
Bibliographic record
Abstract
This report presents the views of participants in a series of workshops on Human Factors (HF) in virtual production planning. The participants, ergonomists and engineers from both public and private sectors, were presented with 6 different Virtual Human Factors (VHF) tools: Discrete Event Simulation, Predetermined Motion Time Systems, Complex and Simple Digital Human Models, Virtual Reality and SIMTER . Comments expressed by participants were recorded on digital audio tapes and by note takers and questionnaires were handed out. Eight main characteristics were identified as influencing factors for the use of VHF tools: cost, time, training, difficulty of use, reliability, graphics, flexibility and usefulness. Other findings included a need to modify report layouts and improvement recommendations particular to each tool. The findings in this report present the initial steps of an ongoing research program with the aim of developing improved approaches to using simulation to integrate human factors proactively into the early stages of a work system design.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".