Exploratory Use of VR Technologies for Training Helicopter Deck-Landing Skills
Bibliographic record
Abstract
Canadian Forces (CF) pilots and landing safety officers require intensive training to develop the individual and team skills required for safe helicopter deck landings. These skills are currently acquired at sea, following individual training with independent simulators unequipped with visual displays. DCIEM is exploring the feasibility of using commercial, off-the-shelf technologies as the essential components for simulators for training the pilot of the Sea King helicopter and the landing safety officer (LSO) of a Canadian Patrol Frigate (CPF). The objective of this project is to assess virtual reality and computer networking technologies that could be exploited in the development of a federation of interconnected, low-cost simulators. The human factors of visual and motion cueing, and coupling of the simulators, present the major technical challenges to the project's success. This paper will describe the exploratory development models, some preliminary reactions, and the experimental plan proposed to assess the training effectiveness of the helicopter simulators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".