Improving calibration time and accuracy for situation-specific models of color differentiation
Bibliographic record
Abstract
Color vision deficiencies (CVDs) cause problems in situations where people need to differentiate the colors used in digital displays. Recoloring tools exist to reduce the problem, but these tools need a model of the user's color-differentiation ability in order to work. Situation-specific models are a recent approach that accounts for all of the factors affecting a person's CVD (including genetic, acquired, and environmental causes) by using calibration data to form the model. This approach works well, but requires repeated calibration - and the best available calibration procedure takes more than 30 minutes. To address this limitation, we have developed a new situation-specific model of human color differentiation (called ICD-2) that needs far fewer calibration trials. The new model uses a color space that better matches human color vision compared to the RGB space of the old model, and can therefore extract more meaning from each calibration test. In an empirical comparison, we found that ICD-2 is 24 times faster than the old approach, and had small but significant gains in accuracy. The efficiency of ICD-2 makes it feasible for situation-specific models of individual color differentiation to be used in the real world.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".