Simulated Night Vision Goggle Wear and Colored Aftereffects
Bibliographic record
Abstract
BACKGROUND: Surveys of military pilots report that between 1.6% and 65% of the respondents experienced altered color vision after night vision goggle (NVG) wear. For the majority of these pilots, the aftereffect was a brownish afterimage that lasted less than 10 min. Given the large disparity in the surveys, we asked subjects to wear goggles which simulated NVGs to determine the nature and duration of any color aftereffects after removing the goggles. METHODS: Two separate experiments were conducted after wearing the goggles for 30 continuous minutes. The first measured the adaptation effects on color appearance by determining the spectral locations of unique blue and unique yellow. The second measured the adaptation effects on color discrimination using the Lanthony Desaturated D15 (Desat D15) color vision test. RESULTS: The location of unique blue shifted to a longer wavelength by 4 nm immediately after removing the goggles and returned to baseline by 12 min post-wear. The unique yellow location was unaffected by the color aftereffect. In the second experiment, the time to complete the Desat D15 was 13% longer than baseline for the first 6 min post-wear. There was also a decrease in the frequency of errors relative to baseline. Only one subject reported an afterimage in either experiment. CONCLUSIONS: The results showed that the color aftereffects were subtle and unlikely to cause major color vision problems. The time course of the color aftereffect in this experiment resembled short-term adaptation effects.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".