Bibliographic record
Abstract
BACKGROUND: Correct identification of wayside signal colors is critical for safe operation of railway equipment. However, evaluating color discrimination using just a screening test may not be occupationally relevant. METHODS: A lantern test (CNLAN) was designed to provide a functional assessment of color discrimination for the rail industry. It was validated against a simulated field trial. 81 individuals with normal color vision and 74 individuals with congenital red-green defects participated. Color vision was classified using the Nagel Anomaloscope. RESULTS: Using a criterion based on the worst-normal performance, 97% of the individuals with a color vision defect failed both the CNLAN and simulation trial. This value is slightly lower than the 100% who failed both the Ishihara test and simulation. However, the Ishihara test also failed 3.7% of the color-normals who passed both the simulation and lantern, whereas by definition none of the color-normals failed the lantern. CONCLUSIONS: This lantern test provides a reasonable functional assessment of one's ability to identify rail signal colors; especially when a strict failing criterion is applied to screening tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".