Advancing vision standards in aviation: Embracing evidence-based approaches
Bibliographic record
Abstract
This study explores the relationship between visual acuity, contrast sensitivity, and pilot performance in simulated flight scenarios, including poor weather conditions, attempting to determine minimum visual requirements for safe flight. Twenty-six participants with normal or corrected-to-normal vision and varying flight experience (0-400 flight hours) completed simulated flight circuits under different weather conditions (e.g., rain, wind) using either Cambridge Simulation Glasses or defocusing lenses to degrade vision. Flight performance was assessed subjectively by an instructor using standardized criteria and objectively via simulator data. Visual acuity and contrast sensitivity were measured at each level of visual degradation. Mixed model analysis of variance revealed significant differences in the variability of vertical speed, pitch, roll, and the slope of altitude descent as a function of vision degradation level and weather conditions. There was also a significant main effect of vision degradation type (scatter or defocus) on the slope of altitude descent. Post hoc analyses indicated flight performance was first affected at 1.0 and 1.3 logarithm of the minimum angle of resolution degradation with scattering and defocusing lenses, respectively. These results suggest that current vision standards should potentially be reevaluated for them to be more based on evidence.
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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 | 0.324 | 0.416 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".