Effects of Motion Filter Parameters on Simulation Fidelity Ratings
Bibliographic record
Abstract
An experiment at NASA Ames' Vertical Motion Simulator (VMS) evaluated simulation motion fidelity using a Bob-up task with a UH60 Blackhawk helicopter model. The experiment used ten different motion cueing configurations that varied the motion gain and washout frequency in the high-pass motion filter located between the aircraft math model and motion system. Ideally the actual aircraft would represent the Baseline configuration, however this can be a prohibitive constraint. The VMS' large motion envelop enabled an unfiltered, one-to-one, motion configuration as a surrogate for the actual aircraft. The Simulation Fidelity Rating (SFR) scale developed by the University of Liverpool and the Canadian National Research Council was used to subjectively rate the motion fidelity. The SFR scale requires the pilot to subjectively compare their performance and technique adaptation in the simulator to that of a baseline. All but two of the configurations tested were characterized as "Fidelity Warrants Improvement" on the SFR scale. The only configuration assessed as "Fit for Purpose" on the SFR scale was the Baseline configuration. The results from the technique adaptation portion of the SFR ratings showed some similarities with the Modified Sinacori Criteria. This indicates that the pilot's technique adaptation level in the simulator may be predicted based on motion filter parameters.
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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 | 0.005 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".