The CN Lantern Test and Different Viewing Distances
Bibliographic record
Abstract
SIGNIFICANCE: This research shows that some color-vision-defective patients could identify railway signal lights correctly if they are working in the yard where sighting distances for signal lights are shorter. PURPOSE: When interpreting railway signal lights, sighting distance can vary depending on the employee's location and job requirements. Individuals with a color-vision-defect may pass railroad employment color vision testing for positions with shorter sighting distances, despite failing to qualify for positions with longer sighting distances. The CN Lantern (CNLan) simulates railway signal lights. We evaluated performance and repeatability on CNLan at different viewing distances in color-normal and color-deficient individuals. METHODS: Fifty-six subjects with normal color vision and 63 subjects with a red-green color-vision-defect participated. The CNLan test was performed at 4.6-, 2.3-, 1.15-, and 0.57-m viewing distance. The test was repeated after 10 days. RESULTS: All individuals with normal color vision passed the CNLan at all distances at both visits without errors. For the group with a color-vision-defect, the pass rate increased from 12% at 4.6 m to 62% at 0.57 m. The repeatability of the CNLan between visits for the color-vision-defective group was very good with AC1 agreement values greater than 0.85. CONCLUSIONS: An increase in retinal illumination was likely responsible for the improved performance as the test distance was decreased. Typical sighting distances in railway yards correspond to the 0.57-m test distance in our study. The results of this study suggest that 62% of the individuals with a red-green color-vision-defect may correctly identify colored signal lights in a railway yard where sighting distances are less than 100 m.
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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 | 0.001 | 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".