Comparison of Pilot Workload between Integrated Reality In-Flight Simulation and Flight Test of Helicopter Landings on a Frigate
Bibliographic record
Abstract
The National Research Council of Canada (NRC) has recently developed an Integrated Reality In-flight Simulator (IRIS) that allows helicopter pilots to fly the NRC's Bell 412 Advanced Systems Research Aircraft (ASRA) while wearing a commercial off-the-shelf (COTS) virtual reality headset. IRIS is the first airborne simulator of its kind that combines COTS virtual reality and Fly-By-Wire (FBW) synthetic turbulence for helicopter operations. Simulations are not exact replications of actual environments; therefore, a methodology of comparing pilot workload with respect to an analysis of the differences between the simulated and actual environments is required. During a recent flight trial, NRC validated the effectiveness of IRIS to replicate a pilot's workload during ship landing tasks using these workload scales. During the analysis, NRC took initial steps in developing methodologies to examine environmental characteristics and then correlate them to an associated pilot workload. The work also included the initial development of methodologies to analyze pilot workload and alternative prediction methods that better map subjective or quantitative pilot workload data to DIPES.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".