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
Individuals with Color Vision Deficiency (CVD) are often unable to distinguish between colors that individuals without CVD can distinguish. Recoloring tools exist that modify the colors in an image so they are more easily distinguishable for those with CVD. These tools use models of color differentiation that rely on many assumptions about the environment and user. However, these assumptions rarely hold in real-world use cases, leading to incorrect color modification by recoloring tools. In this doctoral consortium, I will present Situation-Specific Models (SSMs) as a solution to this problem. SSMs are color differentiation models created in-situ via a calibration procedure. This calibration procedure captures the exact color differentiation abilities of the user, allowing a color differentiation model to be created that fits the user and his/her environmental situation. An SSM-based recoloring tool will be able to provide recolored images that most accurately reflect the color differentiation abilities of a particular individual in a particular environment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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