Visually guided vergence in a new stereo camera system
Bibliographic record
Abstract
People move their eyes several times each second, to selectivelyanalyze visual information from specific locations. This is impor-tant, because analyzing the whole scene in foveal detail would re-quire a beachball-sized brain and thousands of additional caloriesper day. As artificial vision becomes more sophisticated, it mayface analogous constraints. Anticipating this, we previously devel-oped a robotic head with biologically realistic oculomotor capabil-ities. Here we present a system for accurately orienting the cam-eras toward a three-dimensional point. The robot’s cameras con-verge when looking at something nearby, so each camera shouldideally centre the same visual feature. At the end of a saccade,we combine priors with cross-correlation of the images from eachcamera to iteratively fine-tune their alignment, and we use the ori-entations to set focus distance. This system allows the robot toaccurately view a visual target with both eyes.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".