Limitations and Precautions in the Use of the Farnsworth‐Munsell Dichotomous D‐15 Test
Bibliographic record
Abstract
SIGNIFICANCE: Clinicians who administer the Farnsworth-Munsell D-15 test need to pay attention to the quality and quantity of lighting and the time that they allow for completion of the test, and all repeat attempts need to be included in reports on compliance with color vision standards. PURPOSE: The validity of the Farnsworth-Munsell D-15 has been questioned because practice may allow significantly color vision-deficient subjects to pass. In this article, we review the influence of practice and other factors that may affect the performance. These relate to both the design and the administration of the test. METHODS: We review the literature and present some calculations on limitations in the colorimetric design of the test, quantity and quality of lighting, time taken, and repeat attempts. RESULTS: In addition to the review of the literature, color differences and luminance differences under selected sources are calculated, and the increases in luminance clues under some sources and for protanopes are illustrated. CONCLUSIONS: All these factors affect the outcome of the test and need specification and implementation if the test is to be applied consistently and equitably. We recommend the following: practitioners should never rely on a single color vision test regardless of the color vision standard; lighting should be Tcp '' 6500 K and Ra > 90; illuminance levels should be between 200 and 300 lux if detection of color vision deficiency is a priority or between 300 and 1000 lux if the need is to test at the level where illuminance has minimal influence on performance; illuminance should be reported; time limits should be set between 1 and 2 minutes; repeat testing (beyond the specified test and one retest) should be carried out only with authorization; and initial and repeated results should be reported. A set of test instructions to assist in the consistent application of the test is provided in the Appendix.
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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 | 0.176 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".