Flight task performance with high and low gain night vision goggles
Bibliographic record
Abstract
The National Research Council of Canada (NRC) conducted a series of flight tests in collaboration with Transport Canada (TC) to compare manoeuvre performance with high and low gain night vision goggles (NVG). A series of four low-altitude manoeuvres were developed to assess the pilots' ability to perform precision and dynamic tasks with the high and low gain NVG. The manoeuvres consisted of a hover task, a vertical re-mask (bob-up) task, a shallow-descent landing task and a confined area tail-clearing task (tail-turn). To simulate a low gain goggle that would meet currently mandated minimum performance standard, a set of neutral density filters were used to limit light entry. The image presented to the pilot from the filtered NVG approximated the image from a low gain NVG while maintaining other characteristics such as resolution. The subjective data comprised visual cue ratings and a rating of the texture cues apparent in the NVG. The objective data consisted of the measurements of position error for each of the manoeuvres. Examination of the subjective data indicated that the ratings for horizontal and vertical translation cues were significantly better with the high gain NVG than with the low gain NVG. Examination of the objective data showed that only the horizontal position error in the tail-turn task resulted in a statistically significant difference between the low and high gain NVG. Specifically, the pilots tended to drift out of position more when using the low gain NVG than when using the high gain NVG. The results are discussed in terms of NVG contrast and texture perception and the implications for performance standards.
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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 | 0.000 |
| Open science | 0.000 | 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".