Evaluating VR practices to support collaborative cabin design process using a human factor approach
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it