Comparison of Collaborative Display Technologies for Team Design Review
Bibliographic record
Abstract
A comparative evaluation of collaborative display technologies was conducted to explore their ability to support a pair of participants conducting a collaborative workspace design review. Five review media were compared: 2D CAD model on CRT, 3D CAD model on CRT, 3D CAD model on a Curved plasma display, a large DataWall display, and a CAVE environment. Participants reviewed a model depicting an in-vehicle navigation system installed within the front dash of a vehicle and detected design flaws. Performance measures (number of detected flaws and detection time) and usability measures (display, design review, and collaborative quality) were collected. The main findings were: a) flaw detection was better for 3D displays than the 2D display; b) flaws detection was progressively reduced with more immersive 3D displays; c) speed-accuracy tradeoffs were observed such that detection time was less for the 2D than the 3D displays, and decreased with the degree of immersion; d) using the standard CRT is more cost-effective than using the Curved, DataWall, or CAVE displays.
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 | 0.057 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".