Simulator Study of Helmet-Mounted Symbology System Concepts in Degraded Visual Environments
Bibliographic record
Abstract
BACKGROUND: A sudden loss of external visual cues during critical phases of flight results in spatial disorientation. This is due to undetected horizontal and vertical drift when there is little tolerance for error and correction delay as the helicopter is close to the ground. Three helmet-mounted symbology system concepts were investigated in the simulator as potential solutions for the legacy Griffon helicopters. METHOD: Thirteen Royal Canadian Air Force (RCAF) Griffon pilots were exposed to the Helmet Display Tracking System for Degraded Visual Environments (HDTS), the BrownOut Symbology System (BOSS), and the current RCAF AVS7 symbology system. For each symbology system, the pilot performed a two-stage departure and a single-stage approach. The presentation order of the symbology systems was randomized. Objective performance metrics included aircraft speed, altitude, attitude, and distance from the landing point. Subjective measurements included situation awareness, mental effort, perceived performance, perceptual cue rating, and NASA Task Load Index. Repeated measures analysis of variance and subsequent planned comparison for all the objective and subjective measurements were performed between the AVS7, HDTS, and BOSS. RESULTS: Our results demonstrated that HDTS and BOSS showed general improvement over AVS7 in two-stage departure. However, only HDTS performed significantly better in heading error than AVS7. During the single-stage approach, BOSS performed worse than AVS7 in heading root mean square error, and only HDTS performed significantly better in distance to landing point and approach heading than the others. DISCUSSION: Both the HDTS and BOSS possess their own limitations; however, HDTS is the pilots' preferred flight display.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".