Human factors flight trial analysis for 2D situation awareness and 3D synthetic vision displays
Bibliographic record
Abstract
This article describes the human factor analysis from flight trials performed in Reno, NV. Flight trials were conducted with a Cheyenne 1 from Marinvent. Thirteen pilots flew the Cheyenne in seventy-two approaches to the Reno airfield. All pilots flew completely randomized settings. Three different display configurations: 1) Elec. Flight Information System (EFIS), 2) EFIS and 2D moving map, and 3) 3D SVS Primary Flight Display (PFD) and 2D moving map were evaluated. They included normal/abnormal procedure evaluation for: 1) Steep turns and reversals, 2) Unusual attitude recovery, 3) Radar vector guidance towards terrain, 4) Non-precision approaches, 5) En-route alternate for non-IFR rated pilots encountering IMC, and 6) Taxiing on complex taxi-routes. The flight trial goal was to evaluate the objective performance of pilots for the different display configurations. As dependent variables, positional and time data were measured. Analysis was performed by an ANOVA test. In parallel, all pilots answered subjective NASA Task Load Index, Cooper-Harper, Situation Awareness Rating Technique (SART), and questionnaires. The result shows that pilots flying 2D/3D SVS perform no worse than pilots with conventional systems. In addition, 3D SVS flying pilots have significantly better terrain awareness, more stable 180°deg turns, and enhanced positional awareness while taxiing on the ground. Finally, even non-IFR rated pilots are able to fly non-precision approaches under IMC with a 3D SVS.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".