Aviation signal light gun: variations in photometric and colorimetric properties among airports.
Bibliographic record
Abstract
PURPOSE: The aviation signal light gun (LG) is believed by some to be the only color-critical task in aviation without redundant coding. However, there are questions regarding its practicality as a test of color vision given that the brightness and colors may vary between airports. METHODS: The chromaticity coordinates and relative intensities of five LGs were measured with a portable spectroradiometer. Four of the LGs were measured at airports in southern Ontario and compared with a newly purchased LG (ATI Avionics). The air traffic controllers (ATCs) were also surveyed regarding the frequency of LG use. RESULTS: Only 40% of the LGs at the airports were in good working condition. All working LGs met the ICAO standards for airport signal lights. However, differences did exist between models which were related to the date of manufacture. Older LG lights were dimmer and their green and white lights were more yellow than the newer LGs. ATCs reported that they used the LG primarily for pilot instruction and demonstration. However, in two locations, the LG was used to signal pilots who were flying their aircraft in for radio repair. This occurred about once or twice a month. DISCUSSION: The LG is used primarily for instructional purposes. However, if a radio repair shop is at the airport, then the LG will be used about once to twice a month. There is sufficient variability in the light colors and intensity across airports so that any given LG cannot be used as valid practical test of color vision in aviation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".