Exploring gradient-based face navigation interfaces
Why this work is in the frame
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Bibliographic record
Abstract
We have created a gradient-based face navigation interface that allows users to explore a large face space based on an eigenface technique. This approach to synthesizing faces contrasts with more typical techniques for forming composite faces based on the blending of facial features. We compare three ways of moving through the face space, using two types of sliders and a face-wheel. These are adapted from typical color space interfaces since they are commonly used. However, eigenface dimensions do not have meaningful text labels, unlike primary colors, necessitating the use of faces themselves for the labels of the navigation axes. Results suggest that users can navigate with face-labelled axes. They find slider interfaces best suited to finding the neighborhood of a target face, but that the face-wheel is better for refinement once inside the neighborhood.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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