The effect of test distance on the CN lantern results
Bibliographic record
Abstract
The purpose of this study is to determine how the viewing distance affects the pass/fail results of the CN Lantern (CNLan). The CNLan is a color vision test designed for the railway industry. It presents 15 triplets of colored lights that could be any combination of red, green and yellow. The test was viewed from 4.6 m and 2.3 m. Sixty-seven color-defectives participated in the first part of the study. Sixty-six percent of the subjects repeated the experiment 10 days later. There was a significant (P < 0.05) decrease in the mean number of errors from 7.6 to 4.3 as the distance decreased. There was also a corresponding increase in the percentage of subjects who passed from 9.0% at 4.6 m to 20.9% at the 2.3 m viewing distance. None of the subjects who passed at the longer distance failed at the shorter distance. The replication results were statistically identical to the first session (P > 0.05). Decreasing the CNLan viewing distance by 50% does decrease the number of errors and increase the pass rate. This indicates that some color-defectives could work in the railway yards where the sighting distances for the signal lights are shorter than on the main track.
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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 | 0.002 | 0.016 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".