Ability of the D-15 panel tests and HRR pseudoisochromatic plates to predict performance in naming VDT colors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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) | 0.000 | 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 it