Comparison of Reality versus Integrated Reality during In-Flight Maritime Helicopter Hover Manoeuvres
Bibliographic record
Abstract
The National Research Council of Canada (NRC) recently developed an Integrated Reality In-flight Simulator (IRIS) allowing helicopter pilots to fly the NRC's Bell 412 helicopter while wearing a virtual reality headset and experiencing real-world multi-sensory cues. The first IRIS prototype simulated maritime helicopter ship-deck landing operations to augment current flight test and flight training methods. Two NRC test pilots evaluated the IRIS prototype using a customized mission task element (MTE) that represented maritime helicopter hover manoeuvres under three different Visual Cueing Environments (VCEs). Pilot performance and workload was evaluated in each cueing environment along with the effect of simulated airwake turbulence. Both pilots successfully completed the MTE using the Reality Field VCE with moderate workload, but found the Integrated Reality (IR) Field VCE to be higher workload due to poor VR resolution of subtle cues required for longitudinal positioning. By comparison, the pilots successfully completed the MTE using the IR Ship VCE with similar performance levels to the Reality Field. This evaluation demonstrated that the IRIS prototype is suitable for in-flight simulation of shipboard helicopter manoeuvres.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".