<title>Human factors flight trial analysis for 3D SVS: part II</title>
Bibliographic record
Abstract
This paper describes flight trials performed in Centennial, CO using a Piper Cheyenne owned and operated by Marinvent. The goal of the flight trial was to evaluate the objective performance of pilots using conventional paper charts or a 3D SVS display. Six pilots flew thirty-six approaches to the Colorado Springs airport to accomplish this goal. As dependent variables, positional accuracy and situational awareness probe (SAP) statistics were measured while analysis was conducted by an ANOVA test. In parallel, all pilots answered subjective Cooper-Harper, NASA TLX, situation awareness rating technique (SART), Display Readability Rating, Display Flyability Rating and debriefing questionnaires. Three different settings (paper chart, electronic navigation chart, 3D SVS display) were evaluated in a totally randomized manner. This paper describes the comparison between the conventional paper chart and the 3D SVS display. The 3D SVS primary flight display provides a depiction of primary flight data as well as a 3D depiction of airports, terrain and obstacles. In addition, a 3D dynamic channel visualizing the selected approach procedure can be displayed. The result shows that pilots flying the 3D SVS display perform no worse than pilots with the conventional paper chart. Flight technical error and workload are lower, situational awareness is equivalent with conventional paper charts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".