Analysis of subject-dependent point-of-gaze estimation bias in the cross-ratios method
Bibliographic record
Abstract
The cross-ratios method for point-of-gaze estimation uses the invariance property of cross-ratios in projective transformations. The inherent causes of the subject-dependent point-of-gaze estimation bias exhibited by this method have not been well characterized in the literature. Using a model of the eye and the components of a system (camera, light sources) that estimates point-of-gaze, a theoretical framework for the cross-ratios method is developed. The analysis of the cross-ratios method within this framework shows that the subject-dependent estimation bias is caused mainly by (i) the angular deviation of the visual axis from the optic axis and (ii) the fact that the virtual image of the pupil center is not coplanar with the virtual images of the light sources that illuminate the eye (corneal reflections). The theoretical framework provides a closed-form analytical expression that predicts the estimation bias as a function of subject-specific eye parameters.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".