Are ethnic differences in the F‐M 100 scores related to macular pigmentation?
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
BACKGROUND: It is known that the macular pigment can significantly affect colour matching and other aspects of colour vision tests. The difference in macular pigmentation between Asians and Caucasians may lead to different colour discrimination. METHODS: This study compared chromatic discrimination between Asians and Caucasians using the Farnsworth-Munsell 100 Hue test. Fifty Asians who were ethnically Chinese and 50 Caucasians served as subjects, ranging in age from 30 to 59 years. RESULTS: The partial blue-yellow square root error score of the Asian subjects was significantly higher than that of the Caucasian subjects (p = 0.022) and the difference appeared to increase with age. DISCUSSION: There was a difference in the F-M 100 scores between the two groups. The difference was confined in the blue-yellow region, producing a tritan-like bias for the Asian group in the test.
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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 | 0.000 |
| 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) | 0.000 | 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