Repeatability indices for the Farnsworth D-15 test
Bibliographic record
Abstract
The repeatability of the D-15 color-vision test is considered to be excellent. However, this conclusion is based on a subject pool which contained a large percentage of color-normals. This type of sampling could bias the repeatability results because color-normals rarely fail the test. Furthermore, color-normals usually do not perform the D-15 in the clinical setting. To establish the repeatability of the D-15 for a relevant clinical population, we examined the D-15 results from two different sessions for 116 subjects who had a congenital red-green color-vision defect. The kappa coefficient for intersession agreement indicated that approximately 84% of the subjects obtained the same pass/fail results at both sessions. The type of defect was repeatable on approximately 80% of the subjects. Although the repeatability of the D-15 for color-defective subjects was good, it was lower than the near-perfect agreement reported previously. The coefficients of repeatability for the crossings show that if a person makes less than five crossings then the test should be administered again in order to ensure that the test result is repeatable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".