Military Research ColorDx and Printed Color Vision Tests
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
PURPOSE: To determine the equivalence of the ColorDx Military Research version (mColorDx) test and three printed pseudoisochromatic tests (HRR, Ishihara, and PIPIC) for color vision testing. METHODS: Participating in the study were 75 color-normals and 47 subjects with red-green color vision defects. Color vision was classified by an anomaloscope. The HRR (4(th) edition), Ishihara 38-plate edition, and PIPIC tests are printed color vision tests, whereas mColorDx test figures were displayed on a calibrated computer desktop monitor. All tests were repeated in about 1 wk. RESULTS: The kappa level of agreement (κ) values with the anomaloscope for screening for each test was 0.96 or greater. The values were statistically identical. Specificity for each test was at least 0.99 and sensitivity was at least 0.95. The repeatability of the screening sections for all tests was very good with κ values greater than 0.95. Deutans tended to miss the tritan screening plates on the HRR and mColorDx tests. The Spearman rank correlation coefficients between the severity of the defect and anomaloscope range was moderate with r = 0.45 for the mColorDx and r = 0.6 for the HRR. Both the mColorDx and HRR had perfect agreement with the anomaloscope in classifying the defects as either protan or deutan. CONCLUSION: The validity of the four tests for color vision screening was statistically identical; however, the HRR may be preferred because it had the highest sensitivity of 0.99, a specificity of 1.0, and a reasonable correlation between the severity rating of the defect and the anomaloscope range.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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