Evaluating VR practices to support collaborative cabin design process using a human factor approach
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2774.vid Effective collaboration is necessary to overcome complexities in the early stages of aircraft design and development, especially when multiple experts and disciplines are involved in the process. However, there are many barriers in achieving an effective collaborative and multidisciplinary team, such as limited resources and tools, technological limitations, the availability of real time feedback and expertise, and time constrains due to travel and cost. Technological advancements within virtual, augmented and mixed realities provide a unique capability of remote collaborative opportunities that can overcome some of the identified barriers. The National Research Council of Canada (NRC), in close collaboration with the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt - DLR)., proposes to build, use and evaluate a collaborative VR/AR environment that will simulate an aircraft cabin to develop new and disruptive human-centered designs in aircraft cabins. This paper outlines a proposed development of such platform by using human factors approaches to enable remote collaborative work in aircraft cabin design.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".