Ability of the D-15 panel tests and HRR pseudoisochromatic plates to predict performance in naming VDT colors
Bibliographic record
Abstract
Color codes in VDT displays often contain sets of colors that are confusing to individuals with color-vision deficiencies. The purpose of this study is to determine whether individuals with color-vision deficiencies (color defectives) can perform as well as individuals without color-vision deficiencies (color normals) on a colored VDT display used in the railway industry and to determine whether clinical color-vision tests can predict their performance. Of the 52 color defectives, 58% failed the VDT test. The kappa coefficients of agreement for the Farnsworth D-15, Adams desaturated D-15, and Richmond 3rd Edition HRR PIC diagnostic plates were significantly greater than chance. In particular, the D-15 tests have a high probability of predicting who fails the practical test. However, all three tests had an unacceptably high false-negative rate (9.5-35%); so that a practical test is still needed.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".