Integrated Reality In-flight Simulation (IRIS): The Developmental Challenges
Bibliographic record
Abstract
The National Research Council Canada (NRC) and Defence Research and Development Canada (DRDC) have developed modelling and simulation tools intended to de-risk, increase efficiency and standardize Ship Helicopter Operating Limitations (SHOL) testing through the use of modelling and simulation. The Integrated Reality In-Flight Simulation (IRIS) tool is a novel simulator platform where an evaluation pilot (EP) flies a variable stability aircraft while wearing a virtual reality (VR) headset with many elements of the shipboard environment carefully reproduced including the injection of real time turbulence models established from wind tunnel trials. The turbulence and other environmental effects on the aircraft motion are coupled directly into the aircraft in real time, thereby retaining accurate critical proprioceptive and vestibular cueing for the pilot. NRC has recently completed flight test evaluations of the prototype concept. Pilot comments from initial testing indicated that the system required little adaptation, presented no noticeable lag, no attributable propensity to cause motion sickness, and that the turbulence felt realistic. The testing indicated that typical VR deficiencies such as resolution and field of view are not as impactful during close-in shipboard operations, making this application very effective.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".