Navigating Simulator Sickness: The Effect of Flight Maneuvers in Fixed-Base Flight Simulators
Bibliographic record
Abstract
Flight simulator sickness (SS) is a well-known phenomenon in aviation training, which can impact the safety and effectiveness of pilot training programs. Identifying and characterizing which flight maneuvers result in increased SS symptoms could help instructors tailor training to increase pilot retention and potentially improve training. The aim of this study was to explore the impact of different flight maneuvers on SS in a fixed-base simulator (ALSIM AL250). Our results indicate that a flight session with more intense flight maneuvers (landing with wind and taxiing) resulted in an increase in sickness symptoms (Total Sickness [ p = .012] and Oculomotor Disturbance [ p = 0.035] of the SSQ) compared to no changes after a session with less intense flight maneuvers (steep turn). These results demonstrate a need to explore which flight maneuvers are more likely to result in increased sickness symptoms and its effects on training and retention of student pilots.
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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 | 0.001 | 0.006 |
| 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) | 0.001 | 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".