Predicting the CN Lantern Test for Railways with Clinical Color‐vision Tests
Bibliographic record
Abstract
SIGNIFICANCE: This research will help clinicians in advising their color-vision-defective patients regarding their career options. PURPOSE: In Canadian railways, individuals with a color-vision-defect (CVD) may qualify for positions at shorter sighting distance from signal lights. The railway companies' medical units use the CN Lantern (CNLan) test, and there is little information available as to whether clinical color-vision tests (CCVTs) can predict the CNLan results. This study determines the ability of some CCVTs to predict the CNLan performance to assist clinicians in advising their CVD patients regarding career options. METHODS: The CNLan viewing distance was varied between 4.6 and 0.57 m using a geometric progression. The CCVTs were the Hardy, Rand, and Rittler; Ishihara; ColorDx pseudoisochromatic plate (PIP); the Rabin Cone Contrast Test; Color Assessment and Diagnosis; Cambridge Color Vision Test; U.S. Air Force Operational Based Vision Assessment Cone Contrast Test; Farnsworth Munsell D15; and ColorDx D15. Fifty-six normal-color-vision and 63 CVD subjects participated in this study. RESULTS: Failure of either the Farnsworth Munsell D15 or ColorDx D15 essentially guarantees failure on the CNLan at the 4.6-m distance. The agreement values decreased as the viewing distance decreased. CONCLUSIONS: To counsel patients regarding a career as a locomotive engineer, clinicians should have either the Hardy, Rand, and Rittler or ColorDx PIP and a D15 test. For patients applying for a position in the yard, a mild-to-moderate classification CVD on HRR or ColorDx PIP indicates a high probability of passing CNLan.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".